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Martinez-Rodrigo A, Castillo JC, Saz-Lara A, Otero-Luis I, Cavero-Redondo I. Development of a recommendation system and data analysis in personalized medicine: an approach towards healthy vascular ageing. Health Inf Sci Syst 2024; 12:34. [PMID: 38707839 PMCID: PMC11068708 DOI: 10.1007/s13755-024-00292-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Accepted: 04/19/2024] [Indexed: 05/07/2024] Open
Abstract
Purpose Understanding early vascular ageing has become crucial for preventing adverse cardiovascular events. To this respect, recent AI-based risk clustering models offer early detection strategies focused on healthy populations, yet their complexity limits clinical use. This work introduces a novel recommendation system embedded in a web app to assess and mitigate early vascular ageing risk, leading patients towards improved cardiovascular health. Methods This system employs a methodology that calculates distances within multidimensional spaces and integrates cost functions to obtain personalized optimisation of recommendations. It also incorporates a classification system for determining the intensity levels of the clinical interventions. Results The recommendation system showed high efficiency in identifying and visualizing individuals at high risk of early vascular ageing among healthy patients. Additionally, the system corroborated its consistency and reliability in generating personalized recommendations among different levels of granularity, emphasizing its focus on moderate or low-intensity recommendations, which could improve patient adherence to the intervention. Conclusion This tool might significantly aid healthcare professionals in their daily analysis, improving the prevention and management of cardiovascular diseases.
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Affiliation(s)
| | - Jose Carlos Castillo
- Systems Automation and Engineering Department, Carlos III University of Madrid, Madrid, Spain
| | - Alicia Saz-Lara
- Health and Social Research Center, University of Castilla-La Mancha, Cuenca, Spain
| | - Iris Otero-Luis
- Health and Social Research Center, University of Castilla-La Mancha, Cuenca, Spain
| | - Iván Cavero-Redondo
- Health and Social Research Center, University of Castilla-La Mancha, Cuenca, Spain
- Facultad de Ciencias de la Salud, Universidad Autonoma de Chile, Talca, Chile
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2
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Dong W, Wan EYF, Fong DYT, Tan KCB, Tsui WWS, Hui EMT, Chan KH, Fung CSC, Lam CLK. Development and validation of 10-year risk prediction models of cardiovascular disease in Chinese type 2 diabetes mellitus patients in primary care using interpretable machine learning-based methods. Diabetes Obes Metab 2024; 26:3969-3987. [PMID: 39010291 DOI: 10.1111/dom.15745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Revised: 06/03/2024] [Accepted: 06/11/2024] [Indexed: 07/17/2024]
Abstract
AIM To develop 10-year cardiovascular disease (CVD) risk prediction models in Chinese patients with type 2 diabetes mellitus (T2DM) managed in primary care using machine learning (ML) methods. METHODS In this 10-year population-based retrospective cohort study, 141 516 Chinese T2DM patients aged 18 years or above, without history of CVD or end-stage renal disease and managed in public primary care clinics in 2008, were included and followed up until December 2017. Two-thirds of the patients were randomly selected to develop sex-specific CVD risk prediction models. The remaining one-third of patients were used as the validation sample to evaluate the discrimination and calibration of the models. ML-based methods were applied to missing data imputation, predictor selection, risk prediction modelling, model interpretation, and model evaluation. Cox regression was used to develop the statistical models in parallel for comparison. RESULTS During a median follow-up of 9.75 years, 32 445 patients (22.9%) developed CVD. Age, T2DM duration, urine albumin-to-creatinine ratio (ACR), estimated glomerular filtration rate (eGFR), systolic blood pressure variability and glycated haemoglobin (HbA1c) variability were the most important predictors. ML models also identified nonlinear effects of several predictors, particularly the U-shaped effects of eGFR and body mass index. The ML models showed a Harrell's C statistic of >0.80 and good calibration. The ML models performed significantly better than the Cox regression models in CVD risk prediction and achieved better risk stratification for individual patients. CONCLUSION Using routinely available predictors and ML-based algorithms, this study established 10-year CVD risk prediction models for Chinese T2DM patients in primary care. The findings highlight the importance of renal function indicators, and variability in both blood pressure and HbA1c as CVD predictors, which deserve more clinical attention. The derived risk prediction tools have the potential to support clinical decision making and encourage patients towards self-care, subject to further research confirming the models' feasibility, acceptability and applicability at the point of care.
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Affiliation(s)
- Weinan Dong
- Department of Family Medicine and Primary Care, The University of Hong Kong, Hong Kong, China
| | - Eric Yuk Fai Wan
- Department of Family Medicine and Primary Care, The University of Hong Kong, Hong Kong, China
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, The University of Hong Kong, Hong Kong, China
- Advanced Data Analytics for Medical Science (ADAMS) Limited, Hong Kong, China
| | | | | | - Wendy Wing-Sze Tsui
- Department of Family Medicine & Primary Healthcare, Hong Kong West Cluster, Hosptial Authority, Hong Kong, China
| | - Eric Ming-Tung Hui
- Department of Family Medicine, New Territories East Cluster, Hospital Authority, Hong Kong, China
| | - King Hong Chan
- Department of Family Medicine & General Out-patient Clinics, Kowloon Central Cluster, Hospital Authority, Hong Kong, China
| | - Colman Siu Cheung Fung
- Department of Family Medicine and Primary Care, The University of Hong Kong, Hong Kong, China
| | - Cindy Lo Kuen Lam
- Department of Family Medicine and Primary Care, The University of Hong Kong, Hong Kong, China
- Department of Family Medicine, The University of Hong Kong Shenzhen Hospital, Shenzhen, China
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Kamineni M, Raghu V, Truong B, Alaa A, Schuermans A, Friedman S, Reeder C, Bhattacharya R, Libby P, Ellinor PT, Maddah M, Philippakis A, Hornsby W, Yu Z, Natarajan P. Deep learning-derived splenic radiomics, genomics, and coronary artery disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.08.16.24312129. [PMID: 39185532 PMCID: PMC11343250 DOI: 10.1101/2024.08.16.24312129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/27/2024]
Abstract
Background Despite advances in managing traditional risk factors, coronary artery disease (CAD) remains the leading cause of mortality. Circulating hematopoietic cells influence risk for CAD, but the role of a key regulating organ, spleen, is unknown. The understudied spleen is a 3-dimensional structure of the hematopoietic system optimally suited for unbiased radiologic investigations toward novel mechanistic insights. Methods Deep learning-based image segmentation and radiomics techniques were utilized to extract splenic radiomic features from abdominal MRIs of 42,059 UK Biobank participants. Regression analysis was used to identify splenic radiomics features associated with CAD. Genome-wide association analyses were applied to identify loci associated with these radiomics features. Overlap between loci associated with CAD and the splenic radiomics features was explored to understand the underlying genetic mechanisms of the role of the spleen in CAD. Results We extracted 107 splenic radiomics features from abdominal MRIs, and of these, 10 features were associated with CAD. Genome-wide association analysis of CAD-associated features identified 219 loci, including 35 previously reported CAD loci, 7 of which were not associated with conventional CAD risk factors. Notably, variants at 9p21 were associated with splenic features such as run length non-uniformity. Conclusions Our study, combining deep learning with genomics, presents a new framework to uncover the splenic axis of CAD. Notably, our study provides evidence for the underlying genetic connection between the spleen as a candidate causal tissue-type and CAD with insight into the mechanisms of 9p21, whose mechanism is still elusive despite its initial discovery in 2007. More broadly, our study provides a unique application of deep learning radiomics to non-invasively find associations between imaging, genetics, and clinical outcomes.
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He C, Wu F, Fu L, Kong L, Lu Z, Qi Y, Xu H. Improving cardiovascular risk prediction with machine learning: a focus on perivascular adipose tissue characteristics. Biomed Eng Online 2024; 23:77. [PMID: 39098936 PMCID: PMC11299393 DOI: 10.1186/s12938-024-01273-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2024] [Accepted: 07/24/2024] [Indexed: 08/06/2024] Open
Abstract
BACKGROUND Timely prevention of major adverse cardiovascular events (MACEs) is imperative for reducing cardiovascular diseases-related mortality. Perivascular adipose tissue (PVAT), the adipose tissue surrounding coronary arteries, has attracted increased amounts of attention. Developing a model for predicting the incidence of MACE utilizing machine learning (ML) integrating clinical and PVAT features may facilitate targeted preventive interventions and improve patient outcomes. METHODS From January 2017 to December 2019, we analyzed a cohort of 1077 individuals who underwent coronary CT scanning at our facility. Clinical features were collected alongside imaging features, such as coronary artery calcium (CAC) scores and perivascular adipose tissue (PVAT) characteristics. Logistic regression (LR), Framingham Risk Score, and ML algorithms were employed for MACE prediction. RESULTS We screened seven critical features to improve the practicability of the model. MACE patients tended to be older, smokers, and hypertensive. Imaging biomarkers such as CAC scores and PVAT characteristics differed significantly between patients with and without a 3-year MACE risk in a population that did not exhibit disparities in laboratory results. The ensemble model, which leverages multiple ML algorithms, demonstrated superior predictive performance compared with the other models. Finally, the ensemble model was used for risk stratification prediction to explore its clinical application value. CONCLUSIONS The developed ensemble model effectively predicted MACE incidence based on clinical and imaging features, highlighting the potential of ML algorithms in cardiovascular risk prediction and personalized medicine. Early identification of high-risk patients may facilitate targeted preventive interventions and improve patient outcomes.
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Affiliation(s)
- Cong He
- Department of Radiology, Shaoxing Second Hospital, 123 Yanan Rd, Shaoxing, 312000, Zhejiang, China
| | - Fangye Wu
- Department of Radiology, Shaoxing Second Hospital, 123 Yanan Rd, Shaoxing, 312000, Zhejiang, China
| | - Linfeng Fu
- Department of Radiology, Shaoxing Second Hospital, 123 Yanan Rd, Shaoxing, 312000, Zhejiang, China
| | - Lingting Kong
- Department of Radiology, Shaoxing Second Hospital, 123 Yanan Rd, Shaoxing, 312000, Zhejiang, China
| | - Zefeng Lu
- Department of Radiology, Shaoxing Second Hospital, 123 Yanan Rd, Shaoxing, 312000, Zhejiang, China
| | - Yingpeng Qi
- Department of Radiology, Shaoxing Second Hospital, 123 Yanan Rd, Shaoxing, 312000, Zhejiang, China
| | - Hongwei Xu
- Department of Radiology, Shaoxing Second Hospital, 123 Yanan Rd, Shaoxing, 312000, Zhejiang, China.
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Sel K, Osman D, Zare F, Masoumi Shahrbabak S, Brattain L, Hahn JO, Inan OT, Mukkamala R, Palmer J, Paydarfar D, Pettigrew RI, Quyyumi AA, Telfer B, Jafari R. Building Digital Twins for Cardiovascular Health: From Principles to Clinical Impact. J Am Heart Assoc 2024:e031981. [PMID: 39087582 DOI: 10.1161/jaha.123.031981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/02/2024]
Abstract
The past several decades have seen rapid advances in diagnosis and treatment of cardiovascular diseases and stroke, enabled by technological breakthroughs in imaging, genomics, and physiological monitoring, coupled with therapeutic interventions. We now face the challenge of how to (1) rapidly process large, complex multimodal and multiscale medical measurements; (2) map all available data streams to the trajectories of disease states over the patient's lifetime; and (3) apply this information for optimal clinical interventions and outcomes. Here we review new advances that may address these challenges using digital twin technology to fulfill the promise of personalized cardiovascular medical practice. Rooted in engineering mechanics and manufacturing, the digital twin is a virtual representation engineered to model and simulate its physical counterpart. Recent breakthroughs in scientific computation, artificial intelligence, and sensor technology have enabled rapid bidirectional interactions between the virtual-physical counterparts with measurements of the physical twin that inform and improve its virtual twin, which in turn provide updated virtual projections of disease trajectories and anticipated clinical outcomes. Verification, validation, and uncertainty quantification builds confidence and trust by clinicians and patients in the digital twin and establishes boundaries for the use of simulations in cardiovascular medicine. Mechanistic physiological models form the fundamental building blocks of the personalized digital twin that continuously forecast optimal management of cardiovascular health using individualized data streams. We present exemplars from the existing body of literature pertaining to mechanistic model development for cardiovascular dynamics and summarize existing technical challenges and opportunities pertaining to the foundation of a digital twin.
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Affiliation(s)
- Kaan Sel
- Laboratory for Information & Decision Systems (LIDS) Massachusetts Institute of Technology Cambridge MA USA
| | - Deen Osman
- Department of Electrical and Computer Engineering Texas A&M University College Station TX USA
| | - Fatemeh Zare
- Department of Electrical and Computer Engineering Texas A&M University College Station TX USA
| | | | - Laura Brattain
- Lincoln Laboratory Massachusetts Institute of Technology Lexington MA USA
| | - Jin-Oh Hahn
- Department of Mechanical Engineering University of Maryland College Park MD USA
| | - Omer T Inan
- School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA
| | - Ramakrishna Mukkamala
- Department of Bioengineering and Anesthesiology and Perioperative Medicine University of Pittsburgh Pittsburgh PA USA
| | - Jeffrey Palmer
- Lincoln Laboratory Massachusetts Institute of Technology Lexington MA USA
| | - David Paydarfar
- Department of Neurology The University of Texas at Austin Dell Medical School Austin TX USA
| | | | - Arshed A Quyyumi
- Emory Clinical Cardiovascular Research Institute, Division of Cardiology, Department of Medicine Emory University School of Medicine Atlanta GA USA
| | - Brian Telfer
- Lincoln Laboratory Massachusetts Institute of Technology Lexington MA USA
| | - Roozbeh Jafari
- Laboratory for Information & Decision Systems (LIDS) Massachusetts Institute of Technology Cambridge MA USA
- Department of Electrical and Computer Engineering Texas A&M University College Station TX USA
- Lincoln Laboratory Massachusetts Institute of Technology Lexington MA USA
- School of Engineering Medicine Texas A&M University Houston TX USA
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6
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Barkas F, Sener YZ, Golforoush PA, Kheirkhah A, Rodriguez-Sanchez E, Novak J, Apellaniz-Ruiz M, Akyea RK, Bianconi V, Ceasovschih A, Chee YJ, Cherska M, Chora JR, D'Oria M, Demikhova N, Kocyigit Burunkaya D, Rimbert A, Macchi C, Rathod K, Roth L, Sukhorukov V, Stoica S, Scicali R, Storozhenko T, Uzokov J, Lupo MG, van der Vorst EPC, Porsch F. Advancements in risk stratification and management strategies in primary cardiovascular prevention. Atherosclerosis 2024; 395:117579. [PMID: 38824844 DOI: 10.1016/j.atherosclerosis.2024.117579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 04/29/2024] [Accepted: 05/14/2024] [Indexed: 06/04/2024]
Abstract
Atherosclerotic cardiovascular disease (ASCVD) remains a leading cause of morbidity and mortality worldwide, highlighting the urgent need for advancements in risk assessment and management strategies. Although significant progress has been made recently, identifying and managing apparently healthy individuals at a higher risk of developing atherosclerosis and those with subclinical atherosclerosis still poses significant challenges. Traditional risk assessment tools have limitations in accurately predicting future events and fail to encompass the complexity of the atherosclerosis trajectory. In this review, we describe novel approaches in biomarkers, genetics, advanced imaging techniques, and artificial intelligence that have emerged to address this gap. Moreover, polygenic risk scores and imaging modalities such as coronary artery calcium scoring, and coronary computed tomography angiography offer promising avenues for enhancing primary cardiovascular risk stratification and personalised intervention strategies. On the other hand, interventions aiming against atherosclerosis development or promoting plaque regression have gained attention in primary ASCVD prevention. Therefore, the potential role of drugs like statins, ezetimibe, proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors, omega-3 fatty acids, antihypertensive agents, as well as glucose-lowering and anti-inflammatory drugs are also discussed. Since findings regarding the efficacy of these interventions vary, further research is still required to elucidate their mechanisms of action, optimize treatment regimens, and determine their long-term effects on ASCVD outcomes. In conclusion, advancements in strategies addressing atherosclerosis prevention and plaque regression present promising avenues for enhancing primary ASCVD prevention through personalised approaches tailored to individual risk profiles. Nevertheless, ongoing research efforts are imperative to refine these strategies further and maximise their effectiveness in safeguarding cardiovascular health.
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Affiliation(s)
- Fotios Barkas
- Department of Internal Medicine, Faculty of Medicine, School of Health Sciences, University of Ioannina, Ioannina, Greece.
| | - Yusuf Ziya Sener
- Department of Internal Medicine, Faculty of Medicine, Hacettepe University, Ankara, Turkey
| | | | - Azin Kheirkhah
- Institute of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria
| | - Elena Rodriguez-Sanchez
- Division of Cardiology, Department of Medicine, Department of Physiology, and Molecular Biology Institute, UCLA, Los Angeles, CA, USA
| | - Jan Novak
- 2(nd) Department of Internal Medicine, St. Anne's University Hospital in Brno and Faculty of Medicine of Masaryk University, Brno, Czech Republic; Department of Physiology, Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Maria Apellaniz-Ruiz
- Genomics Medicine Unit, Navarra Institute for Health Research - IdiSNA, Navarrabiomed, Hospital Universitario de Navarra (HUN), Universidad Pública de Navarra (UPNA), Pamplona, Spain
| | - Ralph Kwame Akyea
- Centre for Academic Primary Care, School of Medicine, University of Nottingham, United Kingdom
| | - Vanessa Bianconi
- Department of Medicine and Surgery, University of Perugia, Italy
| | - Alexandr Ceasovschih
- Internal Medicine Department, Grigore T. Popa University of Medicine and Pharmacy, Iasi, Romania
| | - Ying Jie Chee
- Department of Endocrinology, Tan Tock Seng Hospital, Singapore
| | - Mariia Cherska
- Cardiology Department, Institute of Endocrinology and Metabolism, Kyiv, Ukraine
| | - Joana Rita Chora
- Unidade I&D, Grupo de Investigação Cardiovascular, Departamento de Promoção da Saúde e Doenças Não Transmissíveis, Instituto Nacional de Saúde Doutor Ricardo Jorge, Lisboa, Portugal; Universidade de Lisboa, Faculdade de Ciências, BioISI - Biosystems & Integrative Sciences Institute, Lisboa, Portugal
| | - Mario D'Oria
- Division of Vascular and Endovascular Surgery, Department of Medical Surgical and Health Sciences, University of Trieste, Trieste, Italy
| | - Nadiia Demikhova
- Sumy State University, Sumy, Ukraine; Tallinn University of Technology, Tallinn, Estonia
| | | | - Antoine Rimbert
- Nantes Université, CNRS, INSERM, l'institut du Thorax, Nantes, France
| | - Chiara Macchi
- Department of Pharmacological and Biomolecular Sciences "Rodolfo Paoletti", Università Degli Studi di Milano, Milan, Italy
| | - Krishnaraj Rathod
- Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom; Barts Interventional Group, Barts Heart Centre, St. Bartholomew's Hospital, London, United Kingdom
| | - Lynn Roth
- Laboratory of Physiopharmacology, University of Antwerp, Antwerp, Belgium
| | - Vasily Sukhorukov
- Laboratory of Cellular and Molecular Pathology of Cardiovascular System, Petrovsky National Research Centre of Surgery, Moscow, Russia
| | - Svetlana Stoica
- "Victor Babes" University of Medicine and Pharmacy, Timisoara, Romania; Institute of Cardiovascular Diseases Timisoara, Timisoara, Romania
| | - Roberto Scicali
- Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy
| | - Tatyana Storozhenko
- Cardiovascular Center Aalst, OLV Clinic, Aalst, Belgium; Department of Prevention and Treatment of Emergency Conditions, L.T. Malaya Therapy National Institute NAMSU, Kharkiv, Ukraine
| | - Jamol Uzokov
- Republican Specialized Scientific Practical Medical Center of Therapy and Medical Rehabilitation, Tashkent, Uzbekistan
| | | | - Emiel P C van der Vorst
- Institute for Molecular Cardiovascular Research (IMCAR), RWTH Aachen University, 52074, Aachen, Germany; Aachen-Maastricht Institute for CardioRenal Disease (AMICARE), RWTH Aachen University, 52074, Aachen, Germany; Institute for Cardiovascular Prevention (IPEK), Ludwig-Maximilians-University Munich, 80336, Munich, Germany; Interdisciplinary Center for Clinical Research (IZKF), RWTH Aachen University, 52074, Aachen, Germany
| | - Florentina Porsch
- Department of Laboratory Medicine, Medical University of Vienna, Vienna, Austria
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Cai YQ, Gong DX, Tang LY, Cai Y, Li HJ, Jing TC, Gong M, Hu W, Zhang ZW, Zhang X, Zhang GW. Pitfalls in Developing Machine Learning Models for Predicting Cardiovascular Diseases: Challenge and Solutions. J Med Internet Res 2024; 26:e47645. [PMID: 38869157 PMCID: PMC11316160 DOI: 10.2196/47645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 10/30/2023] [Accepted: 06/12/2024] [Indexed: 06/14/2024] Open
Abstract
In recent years, there has been explosive development in artificial intelligence (AI), which has been widely applied in the health care field. As a typical AI technology, machine learning models have emerged with great potential in predicting cardiovascular diseases by leveraging large amounts of medical data for training and optimization, which are expected to play a crucial role in reducing the incidence and mortality rates of cardiovascular diseases. Although the field has become a research hot spot, there are still many pitfalls that researchers need to pay close attention to. These pitfalls may affect the predictive performance, credibility, reliability, and reproducibility of the studied models, ultimately reducing the value of the research and affecting the prospects for clinical application. Therefore, identifying and avoiding these pitfalls is a crucial task before implementing the research. However, there is currently a lack of a comprehensive summary on this topic. This viewpoint aims to analyze the existing problems in terms of data quality, data set characteristics, model design, and statistical methods, as well as clinical implications, and provide possible solutions to these problems, such as gathering objective data, improving training, repeating measurements, increasing sample size, preventing overfitting using statistical methods, using specific AI algorithms to address targeted issues, standardizing outcomes and evaluation criteria, and enhancing fairness and replicability, with the goal of offering reference and assistance to researchers, algorithm developers, policy makers, and clinical practitioners.
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Affiliation(s)
- Yu-Qing Cai
- The First Hospital of China Medical University, Shenyang, China
| | - Da-Xin Gong
- Smart Hospital Management Department, The First Hospital of China Medical University, Shenyang, China
| | - Li-Ying Tang
- The First Hospital of China Medical University, Shenyang, China
| | - Yue Cai
- The First Hospital of China Medical University, Shenyang, China
| | - Hui-Jun Li
- Shenyang Medical & Film Science and Technology Co, Ltd, Shenyang, China
| | - Tian-Ci Jing
- Smart Hospital Management Department, The First Hospital of China Medical University, Shenyang, China
| | | | - Wei Hu
- Bayi Orthopedic Hospital, Chengdu, China
| | - Zhen-Wei Zhang
- China Rongtong Medical & Healthcare Co, Ltd, Chengdu, China
| | - Xingang Zhang
- Department of Cardiology, The First Hospital of China Medical University, Shenyang, China
| | - Guang-Wei Zhang
- Smart Hospital Management Department, The First Hospital of China Medical University, Shenyang, China
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8
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Parsa S, Somani S, Dudum R, Jain SS, Rodriguez F. Artificial Intelligence in Cardiovascular Disease Prevention: Is it Ready for Prime Time? Curr Atheroscler Rep 2024; 26:263-272. [PMID: 38780665 DOI: 10.1007/s11883-024-01210-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/08/2024] [Indexed: 05/25/2024]
Abstract
PURPOSE OF REVIEW This review evaluates how Artificial Intelligence (AI) enhances atherosclerotic cardiovascular disease (ASCVD) risk assessment, allows for opportunistic screening, and improves adherence to guidelines through the analysis of unstructured clinical data and patient-generated data. Additionally, it discusses strategies for integrating AI into clinical practice in preventive cardiology. RECENT FINDINGS AI models have shown superior performance in personalized ASCVD risk evaluations compared to traditional risk scores. These models now support automated detection of ASCVD risk markers, including coronary artery calcium (CAC), across various imaging modalities such as dedicated ECG-gated CT scans, chest X-rays, mammograms, coronary angiography, and non-gated chest CT scans. Moreover, large language model (LLM) pipelines are effective in identifying and addressing gaps and disparities in ASCVD preventive care, and can also enhance patient education. AI applications are proving invaluable in preventing and managing ASCVD and are primed for clinical use, provided they are implemented within well-regulated, iterative clinical pathways.
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Affiliation(s)
- Shyon Parsa
- Department of Medicine, Stanford University, Stanford, California, USA
| | - Sulaiman Somani
- Department of Medicine, Stanford University, Stanford, California, USA
| | - Ramzi Dudum
- Division of Cardiovascular Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA
| | - Sneha S Jain
- Division of Cardiovascular Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA
| | - Fatima Rodriguez
- Division of Cardiovascular Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA.
- Center for Digital Health, Stanford University, Stanford, California, USA.
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Shen TT, Liu CF, Wu MP. Implementation of a machine learning model in acute coronary syndrome and stroke risk assessment for patients with lower urinary tract symptoms. Taiwan J Obstet Gynecol 2024; 63:518-526. [PMID: 39004479 DOI: 10.1016/j.tjog.2024.01.037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/03/2024] [Indexed: 07/16/2024] Open
Abstract
OBJECTIVE The global population is aging and the burden of lower urinary tract symptoms (LUTS) is expected to increase. According to the National Health Insurance Research Database, our previous studies have showed LUTS may predispose patients to cardiovascular disease. However, it is difficult to provide a personalized risk assessment in the context of "having acute coronary syndrome (ACS) and stroke." This study aimed to develop an artificial intelligence (AI)-based prediction model for patients with LUTS. MATERIAL AND METHODS We retrospectively reviewed the electronic medical records of 1799 patients with LUTS at Chi Mei Medical Center between January 1, 2001 and December, 31, 2018. Features with >10 cases and high correlations with outcomes were imported into six machine learning algorithms. The study outcomes included ACS and stroke. Model performances was evaluated using the area under the receiver operating characteristic curve (AUC). The model with the highest AUC was used to implement the clinical risk prediction application. RESULTS Age, systemic blood pressure, diastolic blood pressure, creatinine, glycated hemoglobin, hypertension, diabetes mellitus and hyperlipidemia were the most relevant features that affect the outcomes. Based on the AUC, our optimal model was built using multilayer perception (AUC = 0.803) to predict ACS and stroke events within 3 years. CONCLUSION We successfully built an AI-based prediction system that can be used as a prediction model to achieve time-saving, precise, personalized risk evaluation; it can also be used to offer warning, enhance patient adherence, early intervention and better health care outcomes.
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Affiliation(s)
- Tzu-Tsen Shen
- Division of Urogynecology, Department of Obstetrics and Gynecology, Chi Mei Medical Center, Tainan, Taiwan
| | - Chung-Feng Liu
- Department of Medical Research, Chi Mei Medical Center, Tainan, Taiwan
| | - Ming-Ping Wu
- Division of Urogynecology, Department of Obstetrics and Gynecology, Chi Mei Medical Center, Tainan, Taiwan; Department of Post-Baccalaureate Medicine, School of Medicine, College of Medicine, National Sun Yat-sen University, Kaohsiung, Taiwan.
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10
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Zuo W, Yang X. A machine learning model predicts stroke associated with blood cadmium level. Sci Rep 2024; 14:14739. [PMID: 38926494 PMCID: PMC11208606 DOI: 10.1038/s41598-024-65633-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Accepted: 06/21/2024] [Indexed: 06/28/2024] Open
Abstract
Stroke is the leading cause of death and disability worldwide. Cadmium is a prevalent environmental toxicant that may contribute to cardiovascular disease, including stroke. We aimed to build an effective and interpretable machine learning (ML) model that links blood cadmium to the identification of stroke. Our data exploring the association between blood cadmium and stroke came from the National Health and Nutrition Examination Survey (NHANES, 2013-2014). In total, 2664 participants were eligible for this study. We divided these data into a training set (80%) and a test set (20%). To analyze the relationship between blood cadmium and stroke, a multivariate logistic regression analysis was performed. We constructed and tested five ML algorithms including K-nearest neighbor (KNN), decision tree (DT), logistic regression (LR), multilayer perceptron (MLP), and random forest (RF). The best-performing model was selected to identify stroke in US adults. Finally, the features were interpreted using the Shapley Additive exPlanations (SHAP) tool. In the total population, participants in the second, third, and fourth quartiles had an odds ratio of 1.32 (95% CI 0.55, 3.14), 1.65 (95% CI 0.71, 3.83), and 2.67 (95% CI 1.10, 6.49) for stroke compared with the lowest reference group for blood cadmium, respectively. This blood cadmium-based LR approach demonstrated the greatest performance in identifying stroke (area under the operator curve: 0.800, accuracy: 0.966). Employing interpretable methods, we found blood cadmium to be a notable contributor to the predictive model. We found that blood cadmium was positively correlated with stroke risk and that stroke risk from cadmium exposure could be effectively predicted by using ML modeling.
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Affiliation(s)
- Wenwei Zuo
- School of Gongli Hospital Medical Technology, University of Shanghai for Science and Technology, No. 516, Jungong Road, Yangpu Area, Shanghai, 200093, China
| | - Xuelian Yang
- Department of Neurology, Shanghai Pudong New Area Gongli Hospital, No. 219 Miaopu Road, Pudong New Area, Shanghai, 200135, China.
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11
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Gao H, Li J, Ma Q, Zhang Q, Li M, Hu X. Causal Associations of Environmental Pollution and Cardiovascular Disease: A Two-Sample Mendelian Randomization Study. Glob Heart 2024; 19:52. [PMID: 38911616 PMCID: PMC11192098 DOI: 10.5334/gh.1331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 05/18/2024] [Indexed: 06/25/2024] Open
Abstract
Background There is growing evidence that concentrations of environmental pollutants are previously associated with cardiovascular disease; however, it is unclear whether this association reflects a causal relationship. Methods We utilized a two-sample Mendelian randomization (MR) approach to investigate how environmental pollution affects the likelihood of developing cardiovascular disease. We primarily employed the inverse variance weighted (IVW) method. Additionally, to ensure the robustness of our findings, we conducted several sensitivity analyses using alternative methodologies. These included maximum likelihood, MR-Egger regression, weighted median method and weighted model methods. Results Inverse variance weighted estimates suggested that an SD increase in PM2.5 exposure increased the risk of heart failure (OR = 1.40, 95% CI 1.02-1.93, p = 0.0386). We found that an SD increase in PM10 exposure increased the risk of hypertension (OR = 1.45, 95% CI 1.02-2.05, p = 0.03598) and atrial fibrillation (OR = 1.41, 95% CI 1.03-1.94, p = 0.03461). Exposure to chemical or other fumes in a workplace was found to increase the risk of hypertension (OR = 3.08, 95% CI 1.40-6.78, p = 0.005218), coronary artery disease (OR = 1.81, 95% CI 1.00-3.26, p = 0.04861), coronary heart disease (OR = 3.15, 95% CI 1.21-8.16, p = 0.0183) and myocardial infarction (OR = 3.03, 95% CI 1.13-8.17, p = 0.02802). Conclusion This study reveals the causal relationship between air pollutants and cardiovascular diseases, providing new insights into the protection of cardiovascular diseases.
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Affiliation(s)
- Hui Gao
- Department of Cardiovascular Medicine, The First People’s Hospital of Shangqiu, Shangqiu 476000, China
- Graduate School, Dalian Medical University, Dalian, 116044, China
| | - Jiahai Li
- Department of Cardiovascular Medicine, The First People’s Hospital of Qinzhou, Qinzhou 535000, China
| | - Qiaoli Ma
- Department of Cardiovascular Medicine, Central Hospital of Zibo, Zibo 255000, China
| | - Qinghui Zhang
- Department of Hypertension, Henan Provincial People’s Hospital, Zhengzhou 450000, China
| | - Man Li
- Department of Cardiovascular Medicine, The First People’s Hospital of Shangqiu, Shangqiu 476000, China
| | - Xiaoliang Hu
- Department of Cardiovascular Medicine, The First People’s Hospital of Shangqiu, Shangqiu 476000, China
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12
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Du Z, Wang K, Cui Y, Xie X, Zhu R, Dong F, Guo X. The China Hypertrophic Cardiomyopathy Project (CHCMP): The Rationale and Design of a Multicenter, Prospective, Registry Cohort Study. J Cardiovasc Transl Res 2024; 17:732-738. [PMID: 38180696 DOI: 10.1007/s12265-023-10477-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 12/14/2023] [Indexed: 01/06/2024]
Abstract
Hypertrophic cardiomyopathy (HCM) is associated with adverse outcomes, such as heart failure, arrhythmia, and mortality. Sudden cardiac death (SCD) is a common cause of death in HCM patients, and identification of patients at a high risk of SCD is crucial in clinical practice. The China Hypertrophic Cardiomyopathy Project is a hospital-based, multicenter, prospective, registry cohort study of HCM patients, covering a total of 3000 participants and with a 5-year follow-up plan. A large number of demographic characteristics and clinical data will be fully collected to identify prognostic factors in Chinese HCM patients. Furthermore, the main purpose of this study is to integrate demographic and clinical characteristics to establish new 5-year SCD risk predictive equations for Chinese HCM patients by the use of machine learning technologies. The project has crucial clinical significance for risk stratification and determination of HCM patients with high risk of adverse outcomes. CLINICAL TRIALS REGISTRATION: ChiCTR2300070909.
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MESH Headings
- Humans
- Cardiomyopathy, Hypertrophic/mortality
- Cardiomyopathy, Hypertrophic/epidemiology
- Cardiomyopathy, Hypertrophic/therapy
- Cardiomyopathy, Hypertrophic/physiopathology
- Cardiomyopathy, Hypertrophic/diagnosis
- Registries
- Prospective Studies
- China/epidemiology
- Risk Assessment
- Death, Sudden, Cardiac/prevention & control
- Death, Sudden, Cardiac/etiology
- Death, Sudden, Cardiac/epidemiology
- Risk Factors
- Prognosis
- Time Factors
- Male
- Female
- Research Design
- Middle Aged
- Adult
- Multicenter Studies as Topic
- Machine Learning
- Aged
- Young Adult
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Affiliation(s)
- Zhi Du
- Department of Cardiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Kai Wang
- Department of Cardiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yawei Cui
- Department of Cardiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Xudong Xie
- Department of Cardiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Ruoyu Zhu
- Department of Cardiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Fanghong Dong
- Department of Cardiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Xiaogang Guo
- Department of Cardiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
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13
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Bhagawati M, Paul S, Mantella L, Johri AM, Laird JR, Singh IM, Singh R, Garg D, Fouda MM, Khanna NN, Cau R, Abraham A, Al-Maini M, Isenovic ER, Sharma AM, Fernandes JFE, Chaturvedi S, Karla MK, Nicolaides A, Saba L, Suri JS. Deep learning approach for cardiovascular disease risk stratification and survival analysis on a Canadian cohort. THE INTERNATIONAL JOURNAL OF CARDIOVASCULAR IMAGING 2024; 40:1283-1303. [PMID: 38678144 DOI: 10.1007/s10554-024-03100-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 04/02/2024] [Indexed: 04/29/2024]
Abstract
The quantification of carotid plaque has been routinely used to predict cardiovascular risk in cardiovascular disease (CVD) and coronary artery disease (CAD). To determine how well carotid plaque features predict the likelihood of CAD and cardiovascular (CV) events using deep learning (DL) and compare against the machine learning (ML) paradigm. The participants in this study consisted of 459 individuals who had undergone coronary angiography, contrast-enhanced ultrasonography, and focused carotid B-mode ultrasound. Each patient was tracked for thirty days. The measurements on these patients consisted of maximum plaque height (MPH), total plaque area (TPA), carotid intima-media thickness (cIMT), and intraplaque neovascularization (IPN). CAD risk and CV event stratification were performed by applying eight types of DL-based models. Univariate and multivariate analysis was also conducted to predict the most significant risk predictors. The DL's model effectiveness was evaluated by the area-under-the-curve measurement while the CV event prediction was evaluated using the Cox proportional hazard model (CPHM) and compared against the DL-based concordance index (c-index). IPN showed a substantial ability to predict CV events (p < 0.0001). The best DL system improved by 21% (0.929 vs. 0.762) over the best ML system. DL-based CV event prediction showed a ~ 17% increase in DL-based c-index compared to the CPHM (0.86 vs. 0.73). CAD and CV incidents were linked to IPN and carotid imaging characteristics. For survival analysis and CAD prediction, the DL-based system performs superior to ML-based models.
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Affiliation(s)
- Mrinalini Bhagawati
- Department of Biomedical Engineering, North-Eastern Hill University, Shillong, India
| | - Sudip Paul
- Department of Biomedical Engineering, North-Eastern Hill University, Shillong, India
| | - Laura Mantella
- Division of Cardiology, Department of Medicine, University of Toronto, Toronto, Canada
| | - Amer M Johri
- Division of Cardiology, Department of Medicine, Queen's University, Kingston, Canada
| | - John R Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA, 94574, USA
| | - Inder M Singh
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA, 95661, USA
| | - Rajesh Singh
- Division of Research and Innovation, UTI, Uttaranchal University, Dehradun, India
| | - Deepak Garg
- School of Cowereter Science and Artificial Intelligence, SR University, Warangal, Telangana, 506371, India
| | - Mostafa M Fouda
- Department of ECE, Idaho State University, Pocatello, ID, 83209, USA
| | | | - Riccardo Cau
- Department of Radiology, Azienda Ospedaliero Universitaria, 40138, Cagliari, Italy
| | | | - Mostafa Al-Maini
- Allergy, Clinical Immunology and Rheumatology Institute, Toronto, ON, Canada
| | - Esma R Isenovic
- Department of Radiobiology and Molecular Genetics, National Institute of The Republic of Serbia, University of Belgrade, 11001, Belgrade, Serbia
| | - Aditya M Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA, 22904, USA
| | | | - Seemant Chaturvedi
- Department of Neurology & Stroke Program, University of Maryland, Baltimore, MD, USA
| | - Mannudeep K Karla
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre, University of Nicosia Medical School, Nicosia, Cyprus
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria, 40138, Cagliari, Italy
| | - Jasjit S Suri
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA, 95661, USA.
- Department of ECE, Idaho State University, Pocatello, ID, 83209, USA.
- Department of CE, Graphic Era Deemed to be University, 248002, Dehradun, India.
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14
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Rodriguez-Ayllon M, Neumann A, Hofman A, Vernooij MW, Neitzel J. The bidirectional relationship between brain structure and physical activity: A longitudinal analysis in the UK Biobank. Neurobiol Aging 2024; 138:1-9. [PMID: 38460471 DOI: 10.1016/j.neurobiolaging.2024.03.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 02/19/2024] [Accepted: 03/01/2024] [Indexed: 03/11/2024]
Abstract
Physical activity is a protective factor against brain atrophy, while loss of brain volume could also be a determinant of physical activity. Therefore, we aimed to explore the bidirectional association of physical activity with brain structures in middle-aged and older adults from the UK Biobank. Overall, 3027 participants (62.45 ± 7.27 years old, 51.3% females) had data at two time points. Hippocampal volume was associated with total (β=0.048, pFDR=0.016) and household (β=0.075, pFDR<0.001) physical activity. Global fractional anisotropy (β=0.042, pFDR=0.028) was also associated with household physical activity. In the opposite direction, walking was negatively associated with white matter volume (β=-0.026, pFDR=0.008). All these associations were confirmed by the linear mixed models. Interestingly, sports at baseline were linked to hippocampal and frontal cortex volumes at follow-up but these associations disappeared after adjusting for multiple comparisons (pall>0.104). In conclusion, we found more consistent evidence that a healthier brain structure predicted higher physical activity levels than for the inverse, more established relationship.
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Affiliation(s)
- María Rodriguez-Ayllon
- Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Alexander Neumann
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Amy Hofman
- Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Meike W Vernooij
- Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands; Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center Rotterdam, the Netherlands.
| | - Julia Neitzel
- Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands; Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center Rotterdam, the Netherlands; Department of Epidemiology, Harvard T.H Chan School of Public Health, Boston, MA, United States
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15
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Cho JS, Park JH. Application of artificial intelligence in hypertension. Clin Hypertens 2024; 30:11. [PMID: 38689376 PMCID: PMC11061896 DOI: 10.1186/s40885-024-00266-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 02/13/2024] [Indexed: 05/02/2024] Open
Abstract
Hypertension is an important modifiable risk factor for morbidity and mortality associated with cardiovascular disease. The incidence of hypertension is increasing not only in Korea but also in many Western countries due to the aging of the population and the increase in unhealthy lifestyles. However, hypertension control rates remain low due to poor adherence to antihypertensive medications, low awareness of hypertension, and numerous factors that contribute to hypertension, including diet, environment, lifestyle, obesity, and genetics. Because artificial intelligence (AI) involves data-driven algorithms, AI is an asset to understanding chronic diseases that are influenced by multiple factors, such as hypertension. Although several hypertension studies using AI have been published recently, most are exploratory descriptive studies that are often difficult for clinicians to understand and have little clinical relevance. This review aims to provide a clinician-centered perspective on AI by showing recent studies on the relevance of AI for patients with hypertension. The review is organized into sections on blood pressure measurement and hypertension diagnosis, prognosis, and management.
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Affiliation(s)
- Jung Sun Cho
- Division of Cardiology, Daejeon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Catholic Research Institute for Intractable Cardiovascular Disease, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Jae-Hyeong Park
- Department of Cardiology in Internal Medicine, Chungnam National University, Chungnam National University Hospital, 282 Munhwa-ro, Jung-gu, 35015, Daejeon, Republic of Korea.
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16
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St. Pierre SR, Kaczmarski B, Peirlinck M, Kuhl E. Sex-specific cardiovascular risk factors in the UK Biobank. Front Physiol 2024; 15:1339866. [PMID: 39165282 PMCID: PMC11333928 DOI: 10.3389/fphys.2024.1339866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 02/26/2024] [Indexed: 08/22/2024] Open
Abstract
The lack of sex-specific cardiovascular disease criteria contributes to the underdiagnosis of women compared to that of men. For more than half a century, the Framingham Risk Score has been the gold standard to estimate an individual's risk of developing cardiovascular disease based on the age, sex, cholesterol levels, blood pressure, diabetes status, and the smoking status. Now, machine learning can offer a much more nuanced insight into predicting the risk of cardiovascular diseases. The UK Biobank is a large database that includes traditional risk factors and tests related to the cardiovascular system: magnetic resonance imaging, pulse wave analysis, electrocardiograms, and carotid ultrasounds. Here, we leverage 20,542 datasets from the UK Biobank to build more accurate cardiovascular risk models than the Framingham Risk Score and quantify the underdiagnosis of women compared to that of men. Strikingly, for a first-degree atrioventricular block and dilated cardiomyopathy, two conditions with non-sex-specific diagnostic criteria, our study shows that women are under-diagnosed 2× and 1.4× more than men. Similarly, our results demonstrate the need for sex-specific criteria in essential primary hypertension and hypertrophic cardiomyopathy. Our feature importance analysis reveals that out of the top 10 features across three sexes and four disease categories, traditional Framingham factors made up between 40% and 50%; electrocardiogram, 30%-33%; pulse wave analysis, 13%-23%; and magnetic resonance imaging and carotid ultrasound, 0%-10%. Improving the Framingham Risk Score by leveraging big data and machine learning allows us to incorporate a wider range of biomedical data and prediction features, enhance personalization and accuracy, and continuously integrate new data and knowledge, with the ultimate goal to improve accurate prediction, early detection, and early intervention in cardiovascular disease management. Our analysis pipeline and trained classifiers are freely available at https://github.com/LivingMatterLab/CardiovascularDiseaseClassification.
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Affiliation(s)
- Skyler R. St. Pierre
- Department of Mechanical Engineering, Stanford University, Stanford, CA, United States
| | - Bartosz Kaczmarski
- Department of Mechanical Engineering, Stanford University, Stanford, CA, United States
| | - Mathias Peirlinck
- Department of BioMechanical Engineering, Delft University of Technology, Delft, Netherlands
| | - Ellen Kuhl
- Department of Mechanical Engineering, Stanford University, Stanford, CA, United States
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17
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Darabi P, Gharibzadeh S, Khalili D, Bagherpour-Kalo M, Janani L. Optimizing cardiovascular disease mortality prediction: a super learner approach in the tehran lipid and glucose study. BMC Med Inform Decis Mak 2024; 24:97. [PMID: 38627734 PMCID: PMC11020797 DOI: 10.1186/s12911-024-02489-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 03/22/2024] [Indexed: 04/19/2024] Open
Abstract
BACKGROUND & AIM Cardiovascular disease (CVD) is the most important cause of death in the world and has a potential impact on health care costs, this study aimed to evaluate the performance of machine learning survival models and determine the optimum model for predicting CVD-related mortality. METHOD In this study, the research population was all participants in Tehran Lipid and Glucose Study (TLGS) aged over 30 years. We used the Gradient Boosting model (GBM), Support Vector Machine (SVM), Super Learner (SL), and Cox proportional hazard (Cox-PH) models to predict the CVD-related mortality using 26 features. The dataset was randomly divided into training (80%) and testing (20%). To evaluate the performance of the methods, we used the Brier Score (BS), Prediction Error (PE), Concordance Index (C-index), and time-dependent Area Under the Curve (TD-AUC) criteria. Four different clinical models were also performed to improve the performance of the methods. RESULTS Out of 9258 participants with a mean age of (SD; range) 43.74 (15.51; 20-91), 56.60% were female. The CVD death proportion was 2.5% (228 participants). The death proportion was significantly higher in men (67.98% M, 32.02% F). Based on predefined selection criteria, the SL method has the best performance in predicting CVD-related mortality (TD-AUC > 93.50%). Among the machine learning (ML) methods, The SVM has the worst performance (TD-AUC = 90.13%). According to the relative effect, age, fasting blood sugar, systolic blood pressure, smoking, taking aspirin, diastolic blood pressure, Type 2 diabetes mellitus, hip circumference, body mss index (BMI), and triglyceride were identified as the most influential variables in predicting CVD-related mortality. CONCLUSION According to the results of our study, compared to the Cox-PH model, Machine Learning models showed promising and sometimes better performance in predicting CVD-related mortality. This finding is based on the analysis of a large and diverse urban population from Tehran, Iran.
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Affiliation(s)
- Parvaneh Darabi
- Department of Biostatistics, School of Public Health, Iran University of Medical Sciences, Tehran, Iran
| | - Safoora Gharibzadeh
- Department of Epidemiology and Biostatistics, Pasteur Institute of Iran, Tehran, Iran.
| | - Davood Khalili
- Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mehrdad Bagherpour-Kalo
- Department of Epidemiology and Biostatistics, School of Public health, Tehran University of Medical Sciences, Tehran, Iran
| | - Leila Janani
- Department of Biostatistics, School of Public Health, Iran University of Medical Sciences, Tehran, Iran.
- Imperial Clinical Trials Unit, School of Public Health, Imperial College London, London, UK.
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18
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Muse ED, Topol EJ. Transforming the cardiometabolic disease landscape: Multimodal AI-powered approaches in prevention and management. Cell Metab 2024; 36:670-683. [PMID: 38428435 PMCID: PMC10990799 DOI: 10.1016/j.cmet.2024.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 01/25/2024] [Accepted: 02/06/2024] [Indexed: 03/03/2024]
Abstract
The rise of artificial intelligence (AI) has revolutionized various scientific fields, particularly in medicine, where it has enabled the modeling of complex relationships from massive datasets. Initially, AI algorithms focused on improved interpretation of diagnostic studies such as chest X-rays and electrocardiograms in addition to predicting patient outcomes and future disease onset. However, AI has evolved with the introduction of transformer models, allowing analysis of the diverse, multimodal data sources existing in medicine today. Multimodal AI holds great promise in more accurate disease risk assessment and stratification as well as optimizing the key driving factors in cardiometabolic disease: blood pressure, sleep, stress, glucose control, weight, nutrition, and physical activity. In this article we outline the current state of medical AI in cardiometabolic disease, highlighting the potential of multimodal AI to augment personalized prevention and treatment strategies in cardiometabolic disease.
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Affiliation(s)
- Evan D Muse
- Scripps Research Translational Institute, Scripps Research, La Jolla, CA 92037, USA; Division of Cardiovascular Diseases, Scripps Clinic, La Jolla, CA 92037, USA
| | - Eric J Topol
- Scripps Research Translational Institute, Scripps Research, La Jolla, CA 92037, USA; Division of Cardiovascular Diseases, Scripps Clinic, La Jolla, CA 92037, USA.
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19
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Chen X, Zeng Q, Tao L, Yuan J, Hang J, Lu G, Shao J, Li Y, Yu H. Machine Learning-Based Clinical Prediction Models for Acute Ischemic Stroke Based on Serum Xanthine Oxidase Levels. World Neurosurg 2024; 184:e695-e707. [PMID: 38340801 DOI: 10.1016/j.wneu.2024.02.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 02/04/2024] [Indexed: 02/12/2024]
Abstract
OBJECTIVE Early prediction of the onset, progression and prognosis of acute ischemic stroke (AIS) is helpful for treatment decision-making and proactive management. Although several biomarkers have been found to predict the progression and prognosis of AIS, these biomarkers have not been widely used in routine clinical practice. Xanthine oxidase (XO) is a form of xanthine oxidoreductase (XOR), which is widespread in various organs of the human body and plays an important role in redox reactions and ischemia‒reperfusion injury. Our previous studies have shown that serum XO levels on admission have certain clinical predictive value for AIS. The purpose of this study was to utilize serum XO levels and clinical data to establish machine learning models for predicting the onset, progression, and prognosis of AIS. METHODS We enrolled 328 consecutive patients with AIS and 107 healthy controls from October 2020 to September 2021. Serum XO levels and stroke-related clinical data were collected. We established 5 machine learning models-the logistic regression (LR), support vector machine (SVM), decision tree, random forest, and K-nearest neighbor (KNN) models-to predict the onset, progression, and prognosis of AIS. The area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity, negative predictive value, and positive predictive value were used to evaluate the predictive performance of each model. RESULTS Among the 5 machine learning models predicting AIS onset, the AUROC values of 4 prediction models were over 0.7, while that of the KNN model was lower (AUROC = 0.6708, 95% CI 0.576-0.765). The LR model showed the best AUROC value (AUROC = 0.9586, 95% CI 0.927-0.991). Although the 5 machine learning models showed relatively poor predictive value for the progression of AIS (all AUROCs <0.7), the LR model still showed the highest AUROC value (AUROC = 0.6543, 95% CI 0.453-0.856). We compared the value of 5 machine learning models in predicting the prognosis of AIS, and the LR model showed the best predictive value (AUROC = 0.8124, 95% CI 0.715-0.910). CONCLUSIONS The tested machine learning models based on serum levels of XO could predict the onset and prognosis of AIS. Among the 5 machine learning models, we found that the LR model showed the best predictive performance. Machine learning algorithms improve accuracy in the early diagnosis of AIS and can be used to make treatment decisions.
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Affiliation(s)
- Xin Chen
- Clinical Medical College of Yangzhou University, Yangzhou, China; Department of Neuro Intensive Care Unit, Clinical Medical College of Yangzhou University, Yangzhou, China; Department of Neurology, Northern Jiangsu People's Hospital, Yangzhou, China
| | - Qingping Zeng
- School of Nursing, Medical College of Yangzhou University, Yangzhou University, Yangzhou, China
| | - Luhang Tao
- Clinical Medical College of Yangzhou University, Yangzhou, China; Department of Neuro Intensive Care Unit, Clinical Medical College of Yangzhou University, Yangzhou, China; Department of Neurology, Northern Jiangsu People's Hospital, Yangzhou, China
| | - Jing Yuan
- Clinical Medical College of Yangzhou University, Yangzhou, China; Department of Echocardiography, Northern Jiangsu People's Hospital, Yangzhou, China
| | - Jing Hang
- Clinical Medical College of Yangzhou University, Yangzhou, China; Department of Neuro Intensive Care Unit, Clinical Medical College of Yangzhou University, Yangzhou, China; Department of Neurology, Northern Jiangsu People's Hospital, Yangzhou, China
| | - Guangyu Lu
- School of Public Health, Medical College of Yangzhou University, Yangzhou University, Yangzhou, China
| | - Jun Shao
- Clinical Medical College of Yangzhou University, Yangzhou, China; Department of Cardiac Intensive Care Unit, Northern Jiangsu People's Hospital, Yangzhou, China
| | - Yuping Li
- Clinical Medical College of Yangzhou University, Yangzhou, China; Department of Neuro Intensive Care Unit, Clinical Medical College of Yangzhou University, Yangzhou, China; Institute of Neurosurgery, Northern Jiangsu People's Hospital, Yangzhou, China
| | - Hailong Yu
- Clinical Medical College of Yangzhou University, Yangzhou, China; Department of Neuro Intensive Care Unit, Clinical Medical College of Yangzhou University, Yangzhou, China; Department of Neurology, Northern Jiangsu People's Hospital, Yangzhou, China.
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20
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Feuerriegel S, Frauen D, Melnychuk V, Schweisthal J, Hess K, Curth A, Bauer S, Kilbertus N, Kohane IS, van der Schaar M. Causal machine learning for predicting treatment outcomes. Nat Med 2024; 30:958-968. [PMID: 38641741 DOI: 10.1038/s41591-024-02902-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 03/04/2024] [Indexed: 04/21/2024]
Abstract
Causal machine learning (ML) offers flexible, data-driven methods for predicting treatment outcomes including efficacy and toxicity, thereby supporting the assessment and safety of drugs. A key benefit of causal ML is that it allows for estimating individualized treatment effects, so that clinical decision-making can be personalized to individual patient profiles. Causal ML can be used in combination with both clinical trial data and real-world data, such as clinical registries and electronic health records, but caution is needed to avoid biased or incorrect predictions. In this Perspective, we discuss the benefits of causal ML (relative to traditional statistical or ML approaches) and outline the key components and steps. Finally, we provide recommendations for the reliable use of causal ML and effective translation into the clinic.
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Affiliation(s)
- Stefan Feuerriegel
- LMU Munich, Munich, Germany.
- Munich Center for Machine Learning, Munich, Germany.
| | - Dennis Frauen
- LMU Munich, Munich, Germany
- Munich Center for Machine Learning, Munich, Germany
| | - Valentyn Melnychuk
- LMU Munich, Munich, Germany
- Munich Center for Machine Learning, Munich, Germany
| | - Jonas Schweisthal
- LMU Munich, Munich, Germany
- Munich Center for Machine Learning, Munich, Germany
| | - Konstantin Hess
- LMU Munich, Munich, Germany
- Munich Center for Machine Learning, Munich, Germany
| | - Alicia Curth
- Department of Applied Mathematics & Theoretical Physics, University of Cambridge, Cambridge, UK
| | - Stefan Bauer
- School of Computation, Information and Technology, TU Munich, Munich, Germany
- Helmholtz Munich, Munich, Germany
| | - Niki Kilbertus
- Munich Center for Machine Learning, Munich, Germany
- School of Computation, Information and Technology, TU Munich, Munich, Germany
- Helmholtz Munich, Munich, Germany
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Mihaela van der Schaar
- Cambridge Centre for AI in Medicine, University of Cambridge, Cambridge, UK
- The Alan Turing Institute, London, UK
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21
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Layton AT. AI, Machine Learning, and ChatGPT in Hypertension. Hypertension 2024; 81:709-716. [PMID: 38380541 DOI: 10.1161/hypertensionaha.124.19468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2024]
Abstract
Hypertension, a leading cause of cardiovascular disease and premature death, remains incompletely understood despite extensive research. Indeed, even though numerous drugs are available, achieving adequate blood pressure control remains a challenge, prompting recent interest in artificial intelligence. To promote the use of machine learning in cardiovascular medicine, this review provides a brief introduction to machine learning and reviews its notable applications in hypertension management and research, such as disease diagnosis and prognosis, treatment decisions, and omics data analysis. The challenges and limitations associated with data-driven predictive techniques are also discussed. The goal of this review is to raise awareness and encourage the hypertension research community to consider machine learning as a key component in developing innovative diagnostic and therapeutic tools for hypertension. By integrating traditional cardiovascular risk factors with genomics, socioeconomic, behavioral, and environmental factors, machine learning may aid in the development of precise risk prediction models and personalized treatment approaches for patients with hypertension.
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Affiliation(s)
- Anita T Layton
- Department of Applied Mathematics, Department of Biology, Cheriton School of Computer Science, and School of Pharmacology, University of Waterloo, Ontario, Canada
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22
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Thirunavukarasu AJ, Elangovan K, Gutierrez L, Hassan R, Li Y, Tan TF, Cheng H, Teo ZL, Lim G, Ting DSW. Clinical performance of automated machine learning: A systematic review. ANNALS OF THE ACADEMY OF MEDICINE, SINGAPORE 2024; 53:187-207. [PMID: 38920245 DOI: 10.47102/annals-acadmedsg.2023113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/27/2024]
Abstract
Introduction Automated machine learning (autoML) removes technical and technological barriers to building artificial intelligence models. We aimed to summarise the clinical applications of autoML, assess the capabilities of utilised platforms, evaluate the quality of the evidence trialling autoML, and gauge the performance of autoML platforms relative to conventionally developed models, as well as each other. Method This review adhered to a prospectively registered protocol (PROSPERO identifier CRD42022344427). The Cochrane Library, Embase, MEDLINE and Scopus were searched from inception to 11 July 2022. Two researchers screened abstracts and full texts, extracted data and conducted quality assessment. Disagreement was resolved through discussion and if required, arbitration by a third researcher. Results There were 26 distinct autoML platforms featured in 82 studies. Brain and lung disease were the most common fields of study of 22 specialties. AutoML exhibited variable performance: area under the receiver operator characteristic curve (AUCROC) 0.35-1.00, F1-score 0.16-0.99, area under the precision-recall curve (AUPRC) 0.51-1.00. AutoML exhibited the highest AUCROC in 75.6% trials; the highest F1-score in 42.3% trials; and the highest AUPRC in 83.3% trials. In autoML platform comparisons, AutoPrognosis and Amazon Rekognition performed strongest with unstructured and structured data, respectively. Quality of reporting was poor, with a median DECIDE-AI score of 14 of 27. Conclusion A myriad of autoML platforms have been applied in a variety of clinical contexts. The performance of autoML compares well to bespoke computational and clinical benchmarks. Further work is required to improve the quality of validation studies. AutoML may facilitate a transition to data-centric development, and integration with large language models may enable AI to build itself to fulfil user-defined goals.
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Affiliation(s)
- Arun James Thirunavukarasu
- Artificial Intelligence and Digital Innovation Research Group, Singapore Eye Research Institute, Singapore
- University of Cambridge School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Kabilan Elangovan
- Artificial Intelligence and Digital Innovation Research Group, Singapore Eye Research Institute, Singapore
| | - Laura Gutierrez
- Artificial Intelligence and Digital Innovation Research Group, Singapore Eye Research Institute, Singapore
| | - Refaat Hassan
- University of Cambridge School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Yong Li
- Artificial Intelligence and Digital Innovation Research Group, Singapore Eye Research Institute, Singapore
- Duke-NUS Medical School, National University of Singapore, Singapore
| | - Ting Fang Tan
- Artificial Intelligence and Digital Innovation Research Group, Singapore Eye Research Institute, Singapore
| | - Haoran Cheng
- Artificial Intelligence and Digital Innovation Research Group, Singapore Eye Research Institute, Singapore
- Duke-NUS Medical School, National University of Singapore, Singapore
- Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
| | | | - Gilbert Lim
- Artificial Intelligence and Digital Innovation Research Group, Singapore Eye Research Institute, Singapore
| | - Daniel Shu Wei Ting
- Artificial Intelligence and Digital Innovation Research Group, Singapore Eye Research Institute, Singapore
- Duke-NUS Medical School, National University of Singapore, Singapore
- Singapore National Eye Centre, Singapore
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23
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V JP, S AAV, P GK, N K K. A novel attention-based cross-modal transfer learning framework for predicting cardiovascular disease. Comput Biol Med 2024; 170:107977. [PMID: 38217974 DOI: 10.1016/j.compbiomed.2024.107977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 12/19/2023] [Accepted: 01/08/2024] [Indexed: 01/15/2024]
Abstract
Cardiovascular disease (CVD) remains a leading cause of death globally, presenting significant challenges in early detection and treatment. The complexity of CVD arises from its multifaceted nature, influenced by a combination of genetic, environmental, and lifestyle factors. Traditional diagnostic approaches often struggle to effectively integrate and interpret the heterogeneous data associated with CVD. Addressing this challenge, we introduce a novel Attention-Based Cross-Modal (ABCM) transfer learning framework. This framework innovatively merges diverse data types, including clinical records, medical imagery, and genetic information, through an attention-driven mechanism. This mechanism adeptly identifies and focuses on the most pertinent attributes from each data source, thereby enhancing the model's ability to discern intricate interrelationships among various data types. Our extensive testing and validation demonstrate that the ABCM framework significantly surpasses traditional single-source models and other advanced multi-source methods in predicting CVD. Specifically, our approach achieves an accuracy of 93.5%, precision of 92.0%, recall of 94.5%, and an impressive area under the curve (AUC) of 97.2%. These results not only underscore the superior predictive capability of our model but also highlight its potential in offering more accurate and early detection of CVD. The integration of cross-modal data through attention-based mechanisms provides a deeper understanding of the disease, paving the way for more informed clinical decision-making and personalized patient care.
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Affiliation(s)
- Jothi Prakash V
- Karpagam College of Engineering, Myleripalayam Village, Coimbatore, 641032, Tamil Nadu, India.
| | - Arul Antran Vijay S
- Karpagam College of Engineering, Myleripalayam Village, Coimbatore, 641032, Tamil Nadu, India.
| | - Ganesh Kumar P
- College of Engineering, Guindy, Anna University, Chennai, 600025, Tamil Nadu, India.
| | - Karthikeyan N K
- Coimbatore Institute of Technology, Peelamedu, Coimbatore, 641014, Tamil Nadu, India.
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24
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Sasagawa Y, Inoue Y, Futagami K, Nakamura T, Maeda K, Aoki T, Fukubayashi N, Kimoto M, Mizoue T, Hoshina G. Application of deep neural survival networks to the development of risk prediction models for diabetes mellitus, hypertension, and dyslipidemia. J Hypertens 2024; 42:506-514. [PMID: 38088426 PMCID: PMC10842670 DOI: 10.1097/hjh.0000000000003626] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 10/18/2023] [Accepted: 11/07/2023] [Indexed: 02/01/2024]
Abstract
OBJECTIVES : Although numerous risk prediction models have been proposed, few such models have been developed using neural network-based survival analysis. We developed risk prediction models for three cardiovascular disease risk factors (diabetes mellitus, hypertension, and dyslipidemia) among a working-age population in Japan using DeepSurv, a deep feed-forward neural network. METHODS : Data were obtained from the Japan Epidemiology Collaboration on Occupational Health Study. A total of 51 258, 44 197, and 31 452 individuals were included in the development of risk models for diabetes mellitus, hypertension, and dyslipidemia, respectively; two-thirds of whom were used to develop prediction models, and the rest were used to validate the models. We compared the performances of DeepSurv-based models with those of prediction models based on the Cox proportional hazards model. RESULTS : The area under the receiver-operating characteristic curve was 0.878 [95% confidence interval (CI) = 0.864-0.892] for diabetes mellitus, 0.835 (95% CI = 0.826-0.845) for hypertension, and 0.826 (95% CI = 0.817-0.835) for dyslipidemia. Compared with the Cox proportional hazards-based models, the DeepSurv-based models had better reclassification performance [diabetes mellitus: net reclassification improvement (NRI) = 0.474, P ≤ 0.001; hypertension: NRI = 0.194, P ≤ 0.001; dyslipidemia: NRI = 0.397, P ≤ 0.001] and discrimination performance [diabetes mellitus: integrated discrimination improvement (IDI) = 0.013, P ≤ 0.001; hypertension: IDI = 0.007, P ≤ 0.001; and dyslipidemia: IDI = 0.043, P ≤ 0.001]. CONCLUSION : This study suggests that DeepSurv has the potential to improve the performance of risk prediction models for cardiovascular disease risk factors.
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Affiliation(s)
| | - Yosuke Inoue
- Department of Epidemiology and Prevention, National Center for Global Health and Medicine, Tokyo, Japan
| | | | | | | | | | | | | | - Tetsuya Mizoue
- Department of Epidemiology and Prevention, National Center for Global Health and Medicine, Tokyo, Japan
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25
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Cai Y, Cai YQ, Tang LY, Wang YH, Gong M, Jing TC, Li HJ, Li-Ling J, Hu W, Yin Z, Gong DX, Zhang GW. Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review. BMC Med 2024; 22:56. [PMID: 38317226 PMCID: PMC10845808 DOI: 10.1186/s12916-024-03273-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Accepted: 01/23/2024] [Indexed: 02/07/2024] Open
Abstract
BACKGROUND A comprehensive overview of artificial intelligence (AI) for cardiovascular disease (CVD) prediction and a screening tool of AI models (AI-Ms) for independent external validation are lacking. This systematic review aims to identify, describe, and appraise AI-Ms of CVD prediction in the general and special populations and develop a new independent validation score (IVS) for AI-Ms replicability evaluation. METHODS PubMed, Web of Science, Embase, and IEEE library were searched up to July 2021. Data extraction and analysis were performed for the populations, distribution, predictors, algorithms, etc. The risk of bias was evaluated with the prediction risk of bias assessment tool (PROBAST). Subsequently, we designed IVS for model replicability evaluation with five steps in five items, including transparency of algorithms, performance of models, feasibility of reproduction, risk of reproduction, and clinical implication, respectively. The review is registered in PROSPERO (No. CRD42021271789). RESULTS In 20,887 screened references, 79 articles (82.5% in 2017-2021) were included, which contained 114 datasets (67 in Europe and North America, but 0 in Africa). We identified 486 AI-Ms, of which the majority were in development (n = 380), but none of them had undergone independent external validation. A total of 66 idiographic algorithms were found; however, 36.4% were used only once and only 39.4% over three times. A large number of different predictors (range 5-52,000, median 21) and large-span sample size (range 80-3,660,000, median 4466) were observed. All models were at high risk of bias according to PROBAST, primarily due to the incorrect use of statistical methods. IVS analysis confirmed only 10 models as "recommended"; however, 281 and 187 were "not recommended" and "warning," respectively. CONCLUSION AI has led the digital revolution in the field of CVD prediction, but is still in the early stage of development as the defects of research design, report, and evaluation systems. The IVS we developed may contribute to independent external validation and the development of this field.
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Affiliation(s)
- Yue Cai
- China Medical University, Shenyang, 110122, China
| | - Yu-Qing Cai
- China Medical University, Shenyang, 110122, China
| | - Li-Ying Tang
- China Medical University, Shenyang, 110122, China
| | - Yi-Han Wang
- China Medical University, Shenyang, 110122, China
| | - Mengchun Gong
- Digital Health China Co. Ltd, Beijing, 100089, China
| | - Tian-Ci Jing
- Smart Hospital Management Department, the First Hospital of China Medical University, Shenyang, 110001, China
| | - Hui-Jun Li
- Shenyang Medical & Film Science and Technology Co. Ltd., Shenyang, 110001, China
- Enduring Medicine Smart Innovation Research Institute, Shenyang, 110001, China
| | - Jesse Li-Ling
- Institute of Genetic Medicine, School of Life Science, State Key Laboratory of Biotherapy, Sichuan University, Chengdu, 610065, China
| | - Wei Hu
- Bayi Orthopedic Hospital, Chengdu, 610017, China
| | - Zhihua Yin
- Department of Epidemiology, School of Public Health, China Medical University, Shenyang, 110122, China.
| | - Da-Xin Gong
- Smart Hospital Management Department, the First Hospital of China Medical University, Shenyang, 110001, China.
- The Internet Hospital Branch of the Chinese Research Hospital Association, Beijing, 100006, China.
| | - Guang-Wei Zhang
- Smart Hospital Management Department, the First Hospital of China Medical University, Shenyang, 110001, China.
- The Internet Hospital Branch of the Chinese Research Hospital Association, Beijing, 100006, China.
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26
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Lee J, Choi Y, Ko T, Lee K, Shin J, Kim HS. Prediction of Cardiovascular Complication in Patients with Newly Diagnosed Type 2 Diabetes Using an XGBoost/GRU-ODE-Bayes-Based Machine-Learning Algorithm. Endocrinol Metab (Seoul) 2024; 39:176-185. [PMID: 37989268 PMCID: PMC10901655 DOI: 10.3803/enm.2023.1739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 07/22/2023] [Accepted: 08/09/2023] [Indexed: 11/23/2023] Open
Abstract
BACKGRUOUND Cardiovascular disease is life-threatening yet preventable for patients with type 2 diabetes mellitus (T2DM). Because each patient with T2DM has a different risk of developing cardiovascular complications, the accurate stratification of cardiovascular risk is critical. In this study, we proposed cardiovascular risk engines based on machine-learning algorithms for newly diagnosed T2DM patients in Korea. METHODS To develop the machine-learning-based cardiovascular disease engines, we retrospectively analyzed 26,166 newly diagnosed T2DM patients who visited Seoul St. Mary's Hospital between July 2009 and April 2019. To accurately measure diabetes-related cardiovascular events, we designed a buffer (1 year), an observation (1 year), and an outcome period (5 years). The entire dataset was split into training and testing sets in an 8:2 ratio, and this procedure was repeated 100 times. The area under the receiver operating characteristic curve (AUROC) was calculated by 10-fold cross-validation on the training dataset. RESULTS The machine-learning-based risk engines (AUROC XGBoost=0.781±0.014 and AUROC gated recurrent unit [GRU]-ordinary differential equation [ODE]-Bayes=0.812±0.016) outperformed the conventional regression-based model (AUROC=0.723± 0.036). CONCLUSION GRU-ODE-Bayes-based cardiovascular risk engine is highly accurate, easily applicable, and can provide valuable information for the individualized treatment of Korean patients with newly diagnosed T2DM.
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Affiliation(s)
- Joonyub Lee
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | | | - Taehoon Ko
- Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Kanghyuck Lee
- Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, Korea
- Department of Biomedicine and Health Sciences, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Juyoung Shin
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
- Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, Korea
- Health Promotion Center, Seoul St. Mary’s Hospital, Seoul, Korea
| | - Hun-Sung Kim
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
- Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, Korea
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27
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Giddings R, Joseph A, Callender T, Janes SM, van der Schaar M, Sheringham J, Navani N. Factors influencing clinician and patient interaction with machine learning-based risk prediction models: a systematic review. Lancet Digit Health 2024; 6:e131-e144. [PMID: 38278615 DOI: 10.1016/s2589-7500(23)00241-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 10/20/2023] [Accepted: 11/14/2023] [Indexed: 01/28/2024]
Abstract
Machine learning (ML)-based risk prediction models hold the potential to support the health-care setting in several ways; however, use of such models is scarce. We aimed to review health-care professional (HCP) and patient perceptions of ML risk prediction models in published literature, to inform future risk prediction model development. Following database and citation searches, we identified 41 articles suitable for inclusion. Article quality varied with qualitative studies performing strongest. Overall, perceptions of ML risk prediction models were positive. HCPs and patients considered that models have the potential to add benefit in the health-care setting. However, reservations remain; for example, concerns regarding data quality for model development and fears of unintended consequences following ML model use. We identified that public views regarding these models might be more negative than HCPs and that concerns (eg, extra demands on workload) were not always borne out in practice. Conclusions are tempered by the low number of patient and public studies, the absence of participant ethnic diversity, and variation in article quality. We identified gaps in knowledge (particularly views from under-represented groups) and optimum methods for model explanation and alerts, which require future research.
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Affiliation(s)
- Rebecca Giddings
- Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK.
| | - Anabel Joseph
- Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK
| | - Thomas Callender
- Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK
| | - Sam M Janes
- Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK
| | - Mihaela van der Schaar
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK; The Alan Turing Institute, London, UK
| | - Jessica Sheringham
- Department of Applied Health Research, University College London, London, UK
| | - Neal Navani
- Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK
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28
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Akyüz K, Cano Abadía M, Goisauf M, Mayrhofer MT. Unlocking the potential of big data and AI in medicine: insights from biobanking. Front Med (Lausanne) 2024; 11:1336588. [PMID: 38357641 PMCID: PMC10864616 DOI: 10.3389/fmed.2024.1336588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Accepted: 01/19/2024] [Indexed: 02/16/2024] Open
Abstract
Big data and artificial intelligence are key elements in the medical field as they are expected to improve accuracy and efficiency in diagnosis and treatment, particularly in identifying biomedically relevant patterns, facilitating progress towards individually tailored preventative and therapeutic interventions. These applications belong to current research practice that is data-intensive. While the combination of imaging, pathological, genomic, and clinical data is needed to train algorithms to realize the full potential of these technologies, biobanks often serve as crucial infrastructures for data-sharing and data flows. In this paper, we argue that the 'data turn' in the life sciences has increasingly re-structured major infrastructures, which often were created for biological samples and associated data, as predominantly data infrastructures. These have evolved and diversified over time in terms of tackling relevant issues such as harmonization and standardization, but also consent practices and risk assessment. In line with the datafication, an increased use of AI-based technologies marks the current developments at the forefront of the big data research in life science and medicine that engender new issues and concerns along with opportunities. At a time when secure health data environments, such as European Health Data Space, are in the making, we argue that such meta-infrastructures can benefit both from the experience and evolution of biobanking, but also the current state of affairs in AI in medicine, regarding good governance, the social aspects and practices, as well as critical thinking about data practices, which can contribute to trustworthiness of such meta-infrastructures.
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Affiliation(s)
- Kaya Akyüz
- Department of ELSI Services and Research, BBMRI-ERIC, Graz, Austria
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29
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Menotti A, Puddu PE. In Search of Risk Factors: The Origin and Early Stages of Cardiovascular Epidemiology. J Cardiovasc Dev Dis 2024; 11:20. [PMID: 38248890 PMCID: PMC10816974 DOI: 10.3390/jcdd11010020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 01/06/2024] [Accepted: 01/09/2024] [Indexed: 01/23/2024] Open
Abstract
Based mainly on their personal experience, the authors try to describe the origin of cardiovascular disease (CVD) epidemiology and the problems and difficulties practitioners attempted to tackle and solve during the first few decades of this discipline, which started around the middle of the last century. Beyond identifying the characteristics of those who became CVD epidemiologists, a description is given of the initial structures of the involved studies, participation rates, risk factors measurements and standardization, clinical measurements and diagnostic criteria, mortality data collection and coding, data loading and analysis, plus a number of problems still unsolved at the beginning of the 2000s. Despite many obstacles, and the initial hostility of the medical-scientific establishment, CVD epidemiology represented a revolution in researching in the bio-medical field. In the end, it also affected clinical research introducing the use of the quantitative approach bound to mathematical-statistical procedures. After decades of hard work and the development of a number of innovative tools, CVD epidemiology received its deserved recognition, eventually being accepted as a reputable and independent scientific discipline. Yet, in several countries, especially those from Southern Europe, an academic recognition of CVD epidemiology is still lacking.
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30
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Lin E, Lin CH, Lane HY. Inference of social cognition in schizophrenia patients with neurocognitive domains and neurocognitive tests using automated machine learning. Asian J Psychiatr 2024; 91:103866. [PMID: 38128351 DOI: 10.1016/j.ajp.2023.103866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 12/07/2023] [Accepted: 12/09/2023] [Indexed: 12/23/2023]
Abstract
AIM It has been suggested that single neurocognitive domain or neurocognitive test can be used to determine the overall cognitive function in schizophrenia using machine learning algorithms. It is unknown whether social cognition in schizophrenia patients can be estimated with machine learning based on neurocognitive domains or neurocognitive tests. METHODS To predict social cognition in schizophrenia, we applied an automated machine learning (AutoML) framework resulting from the analysis of predictive factors such as six neurocognitive domain scores and nine neurocognitive test scores of 380 schizophrenia patients in the Taiwanese population. Four clinical parameters (i.e., age, gender, subgroup, and education) were also used as predictive factors. We utilized an AutoML framework called Tree-based Pipeline Optimization Tool (TPOT) to generate predictive pipelines automatically. RESULTS The analysis revealed that all neurocognitive domains and tests except the reasoning and problem solving domain/test showed significant associations with social cognition. In addition, a TPOT-generated pipeline can best predict social cognition in schizophrenia using seven predictive factors, including five neurocognitive domains (i.e., speed of processing, sustained attention, working memory, verbal learning and memory, and visual learning and memory) and two clinical parameters (i.e., age and gender). This predictive pipeline consists of machine learning algorithms such as function transformers, an approximate feature map, independent component analysis, and linear regression. CONCLUSION The study indicates that an AutoML framework such as TPOT may provide a promising way to produce truly effective machine learning pipelines for predicting social cognition in schizophrenia using neurocognitive domains and/or neurocognitive tests.
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Affiliation(s)
- Eugene Lin
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA; Department of Electrical & Computer Engineering, University of Washington, Seattle, WA 98195, USA; Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan
| | - Chieh-Hsin Lin
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan; Department of Psychiatry, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan; School of Medicine, Chang Gung University, Taoyuan, Taiwan.
| | - Hsien-Yuan Lane
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan; Department of Psychiatry, China Medical University Hospital, Taichung, Taiwan; Brain Disease Research Center, China Medical University Hospital, Taichung, Taiwan; Department of Psychology, College of Medical and Health Sciences, Asia University, Taichung, Taiwan.
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31
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You J, Guo Y, Kang JJ, Wang HF, Yang M, Feng JF, Yu JT, Cheng W. Development of machine learning-based models to predict 10-year risk of cardiovascular disease: a prospective cohort study. Stroke Vasc Neurol 2023; 8:475-485. [PMID: 37105576 PMCID: PMC10800279 DOI: 10.1136/svn-2023-002332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 04/03/2023] [Indexed: 04/29/2023] Open
Abstract
BACKGROUND Previous prediction algorithms for cardiovascular diseases (CVD) were established using risk factors retrieved largely based on empirical clinical knowledge. This study sought to identify predictors among a comprehensive variable space, and then employ machine learning (ML) algorithms to develop a novel CVD risk prediction model. METHODS From a longitudinal population-based cohort of UK Biobank, this study included 473 611 CVD-free participants aged between 37 and 73 years old. We implemented an ML-based data-driven pipeline to identify predictors from 645 candidate variables covering a comprehensive range of health-related factors and assessed multiple ML classifiers to establish a risk prediction model on 10-year incident CVD. The model was validated through a leave-one-center-out cross-validation. RESULTS During a median follow-up of 12.2 years, 31 466 participants developed CVD within 10 years after baseline visits. A novel UK Biobank CVD risk prediction (UKCRP) model was established that comprised 10 predictors including age, sex, medication of cholesterol and blood pressure, cholesterol ratio (total/high-density lipoprotein), systolic blood pressure, previous angina or heart disease, number of medications taken, cystatin C, chest pain and pack-years of smoking. Our model obtained satisfied discriminative performance with an area under the receiver operating characteristic curve (AUC) of 0.762±0.010 that outperformed multiple existing clinical models, and it was well-calibrated with a Brier Score of 0.057±0.006. Further, the UKCRP can obtain comparable performance for myocardial infarction (AUC 0.774±0.011) and ischaemic stroke (AUC 0.730±0.020), but inferior performance for haemorrhagic stroke (AUC 0.644±0.026). CONCLUSION ML-based classification models can learn expressive representations from potential high-risked CVD participants who may benefit from earlier clinical decisions.
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Affiliation(s)
- Jia You
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Yu Guo
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Ju-Jiao Kang
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Hui-Fu Wang
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Ming Yang
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Jian-Feng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
- Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, China
- School of Data Science, Fudan University, Shanghai, China
- Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Zhejiang, China
| | - Jin-Tai Yu
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Wei Cheng
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
- Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Zhejiang, China
- Shanghai Medical College and Zhongshan Hospital Immunotherapy Technology Transfer Center, Fudan University, Shanghai, China
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KolaszyŃSka O, Lorkowski J. Symmetry and asymmetry in atherosclerosis. Int J Occup Med Environ Health 2023; 36:693-703. [PMID: 37791506 PMCID: PMC10743353 DOI: 10.13075/ijomeh.1896.02171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 08/11/2023] [Indexed: 10/05/2023] Open
Abstract
Atherosclerosis remains the main cause of death worldwide. Most important issues concerning atherosclerosis are hemodynamics and how it affects plaque prevalence and distribution, as well as the symmetry and asymmetry of vasculature and plaques. To present the symmetry in the vascular system an analysis of PubMed and MEDLINE databases was performed. As of February 21, 2023, the results were as follows: for "symmetry" AND "atherosclerosis" there were 47 results; for "symmetry" AND "atherosclerotic lesions" - 20 results; for "symmetry" AND "artery stenosis" - 82 results; for "asymmetry" AND "atherosclerosis" - 87 results. Not without meaning are preventive measures. In the light of the Fourth Industrial Revolution artificial intelligence (AI) solutions help to develop new tools outperforming already existing cardiovascular risk scales. The aim of this paper is to present a current view on symmetry within vasculature and atherosclerosis as well as present a new approach to assess individuals' cardiovascular risk in accordance with precision medicine assumptions. Symmetry and asymmetry within the human vascular system play a crucial role in understanding of arterial diseases, including atherosclerosis. Moreover, it is unavoidable to use AI in cardiovascular risk stratification. Int J Occup Med Environ Health. 2023;36(6):693-703.
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Affiliation(s)
- Oliwia KolaszyŃSka
- Asklepios Klinikum Uckermark, I Department of Internal Medicine, Schwedt, Germany
| | - Jacek Lorkowski
- Central Clinical Hospital of Interior and Administration, Department of Orthopedics, Traumatology and Sports Medicine, Warsaw, Poland
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Gautam N, Mueller J, Alqaisi O, Gandhi T, Malkawi A, Tarun T, Alturkmani HJ, Zulqarnain MA, Pontone G, Al'Aref SJ. Machine Learning in Cardiovascular Risk Prediction and Precision Preventive Approaches. Curr Atheroscler Rep 2023; 25:1069-1081. [PMID: 38008807 DOI: 10.1007/s11883-023-01174-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/15/2023] [Indexed: 11/28/2023]
Abstract
PURPOSE OF REVIEW In this review, we sought to provide an overview of ML and focus on the contemporary applications of ML in cardiovascular risk prediction and precision preventive approaches. We end the review by highlighting the limitations of ML while projecting on the potential of ML in assimilating these multifaceted aspects of CAD in order to improve patient-level outcomes and further population health. RECENT FINDINGS Coronary artery disease (CAD) is estimated to affect 20.5 million adults across the USA, while also impacting a significant burden at the socio-economic level. While the knowledge of the mechanistic pathways that govern the onset and progression of clinical CAD has improved over the past decade, contemporary patient-level risk models lag in accuracy and utility. Recently, there has been renewed interest in combining advanced analytic techniques that utilize artificial intelligence (AI) with a big data approach in order to improve risk prediction within the realm of CAD. By virtue of being able to combine diverse amounts of multidimensional horizontal data, machine learning has been employed to build models for improved risk prediction and personalized patient care approaches. The use of ML-based algorithms has been used to leverage individualized patient-specific data and the associated metabolic/genomic profile to improve CAD risk assessment. While the tool can be visualized to shift the paradigm toward a patient-specific care, it is crucial to acknowledge and address several challenges inherent to ML and its integration into healthcare before it can be significantly incorporated in the daily clinical practice.
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Affiliation(s)
- Nitesh Gautam
- Division of Cardiology, Department of Internal Medicine, University of Arkansas for Medical Sciences, 4301 W. Markham St, Little Rock, AR, 72223, USA
| | - Joshua Mueller
- Department of Internal Medicine, University of Arkansas for Medical Sciences Northwest Regional Campus, Fayetteville, AR, USA
| | - Omar Alqaisi
- Department of Internal Medicine, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Tanmay Gandhi
- Department of Internal Medicine, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Abdallah Malkawi
- Division of Cardiology, Department of Internal Medicine, University of Arkansas for Medical Sciences, 4301 W. Markham St, Little Rock, AR, 72223, USA
| | - Tushar Tarun
- Division of Cardiology, Department of Internal Medicine, University of Arkansas for Medical Sciences, 4301 W. Markham St, Little Rock, AR, 72223, USA
| | - Hani J Alturkmani
- Division of Cardiology, Department of Internal Medicine, University of Arkansas for Medical Sciences, 4301 W. Markham St, Little Rock, AR, 72223, USA
| | - Muhammed Ali Zulqarnain
- Division of Cardiology, Department of Internal Medicine, University of Arkansas for Medical Sciences, 4301 W. Markham St, Little Rock, AR, 72223, USA
| | | | - Subhi J Al'Aref
- Division of Cardiology, Department of Internal Medicine, University of Arkansas for Medical Sciences, 4301 W. Markham St, Little Rock, AR, 72223, USA.
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Newby D, Orgeta V, Marshall CR, Lourida I, Albertyn CP, Tamburin S, Raymont V, Veldsman M, Koychev I, Bauermeister S, Weisman D, Foote IF, Bucholc M, Leist AK, Tang EYH, Tai XY, Llewellyn DJ, Ranson JM. Artificial intelligence for dementia prevention. Alzheimers Dement 2023; 19:5952-5969. [PMID: 37837420 PMCID: PMC10843720 DOI: 10.1002/alz.13463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 08/01/2023] [Accepted: 08/07/2023] [Indexed: 10/16/2023]
Abstract
INTRODUCTION A wide range of modifiable risk factors for dementia have been identified. Considerable debate remains about these risk factors, possible interactions between them or with genetic risk, and causality, and how they can help in clinical trial recruitment and drug development. Artificial intelligence (AI) and machine learning (ML) may refine understanding. METHODS ML approaches are being developed in dementia prevention. We discuss exemplar uses and evaluate the current applications and limitations in the dementia prevention field. RESULTS Risk-profiling tools may help identify high-risk populations for clinical trials; however, their performance needs improvement. New risk-profiling and trial-recruitment tools underpinned by ML models may be effective in reducing costs and improving future trials. ML can inform drug-repurposing efforts and prioritization of disease-modifying therapeutics. DISCUSSION ML is not yet widely used but has considerable potential to enhance precision in dementia prevention. HIGHLIGHTS Artificial intelligence (AI) is not widely used in the dementia prevention field. Risk-profiling tools are not used in clinical practice. Causal insights are needed to understand risk factors over the lifespan. AI will help personalize risk-management tools for dementia prevention. AI could target specific patient groups that will benefit most for clinical trials.
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Affiliation(s)
- Danielle Newby
- University of Oxford, Department of Psychiatry, Warneford Hospital, Oxford, OX3 7JX, UK
| | - Vasiliki Orgeta
- Division of Psychiatry, University College London, London, W1T 7BN, UK
| | - Charles R Marshall
- Preventive Neurology Unit, Wolfson Institute of Population Health, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, E1 4NS, UK
- Department of Neurology, Royal London Hospital, London, E1 1BB, UK
| | - Ilianna Lourida
- Population Health Sciences Institute, Newcastle University, Newcastle, NE2 4AX, UK
- University of Exeter Medical School, Exeter, EX1 2HZ, UK
| | - Christopher P Albertyn
- Department of Old Age Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, SE5 8AF, UK
| | - Stefano Tamburin
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, 37129, Italy
| | - Vanessa Raymont
- University of Oxford, Department of Psychiatry, Warneford Hospital, Oxford, OX3 7JX, UK
| | - Michele Veldsman
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, OX3 9DU, UK
- Department of Experimental Psychology, University of Oxford, Oxford, OX2 6GG, UK
| | - Ivan Koychev
- University of Oxford, Department of Psychiatry, Warneford Hospital, Oxford, OX3 7JX, UK
| | - Sarah Bauermeister
- University of Oxford, Department of Psychiatry, Warneford Hospital, Oxford, OX3 7JX, UK
| | - David Weisman
- Abington Neurological Associates, Abington, PA 19001, USA
| | - Isabelle F Foote
- Preventive Neurology Unit, Wolfson Institute of Population Health, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, E1 4NS, UK
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO 80309, USA
| | - Magda Bucholc
- Cognitive Analytics Research Lab, School of Computing, Engineering & Intelligent Systems, Ulster University, Derry, BT48 7JL, UK
| | - Anja K Leist
- Institute for Research on Socio-Economic Inequality (IRSEI), Department of Social Sciences, University of Luxembourg, L-4365, Luxembourg
| | - Eugene Y H Tang
- Population Health Sciences Institute, Newcastle University, Newcastle, NE2 4AX, UK
| | - Xin You Tai
- Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, OX3 9DU, UK
- Division of Clinical Neurology, John Radcliffe Hospital, Oxford University Hospitals Trust, Oxford, OX3 9DU, UK
| | | | - David J. Llewellyn
- University of Exeter Medical School, Exeter, EX1 2HZ, UK
- The Alan Turing Institute, London, NW1 2DB, UK
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Ali MM, Gandhi S, Sulaiman S, Jafri SH, Ali AS. Mapping the Heartbeat of America with ChatGPT-4: Unpacking the Interplay of Social Vulnerability, Digital Literacy, and Cardiovascular Mortality in County Residency Choices. J Pers Med 2023; 13:1625. [PMID: 38138852 PMCID: PMC10744376 DOI: 10.3390/jpm13121625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 10/31/2023] [Accepted: 11/16/2023] [Indexed: 12/24/2023] Open
Abstract
Cardiovascular disease remains a leading cause of morbidity and mortality in the United States (US). Although high-quality data are accessible in the US for cardiovascular research, digital literacy (DL) has not been explored as a potential factor influencing cardiovascular mortality, although the Social Vulnerability Index (SVI) has been used previously as a variable in predictive modeling. Utilizing a large language model, ChatGPT4, we investigated the variability in CVD-specific mortality that could be explained by DL and SVI using regression modeling. We fitted two models to calculate the crude and adjusted CVD mortality rates. Mortality data using ICD-10 codes were retrieved from CDC WONDER, and the geographic level data was retrieved from the US Department of Agriculture. Both datasets were merged using the Federal Information Processing Standards code. The initial exploration involved data from 1999 through 2020 (n = 65,791; 99.98% complete for all US Counties) for crude cardiovascular mortality (CCM). Age-adjusted cardiovascular mortality (ACM) had data for 2020 (n = 3118 rows; 99% complete for all US Counties), with the inclusion of SVI and DL in the model (a composite of literacy and internet access). By leveraging on the advanced capabilities of ChatGPT4 and linear regression, we successfully highlighted the importance of incorporating the SVI and DL in predicting adjusted cardiovascular mortality. Our findings imply that just incorporating internet availability in the regression model may not be sufficient without incorporating significant variables, such as DL and SVI, to predict ACM. Further, our approach could enable future researchers to consider DL and SVI as key variables to study other health outcomes of public-health importance, which could inform future clinical practices and policies.
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Affiliation(s)
- Mohammed M. Ali
- Multidisciplinary Studies Programs, Eberly College of Arts and Sciences, West Virginia University, Morgantown, WV 26506, USA;
| | - Subi Gandhi
- Department of Medical Lab Sciences, Public Health and Nutrition Science, Tarleton State University, 1333 West Washington, Stephenville, TX 76402, USA;
| | - Samian Sulaiman
- Department of Cardiology, Heart and Vascular Institute, West Virginia University, 1 Medical Center Drive, Morgantown, WV 26501, USA;
| | - Syed H. Jafri
- Department of Accounting, Finance and Economics, Tarleton State University, 1333 West Washington, Stephenville, TX 76402, USA;
| | - Abbas S. Ali
- Department of Cardiology, Heart and Vascular Institute, West Virginia University, 1 Medical Center Drive, Morgantown, WV 26501, USA;
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Morris MC, Moradi H, Aslani M, Sims M, Schlundt D, Kouros CD, Goodin B, Lim C, Kinney K. Predicting incident cardiovascular disease among African-American adults: A deep learning approach to evaluate social determinants of health in the Jackson heart study. PLoS One 2023; 18:e0294050. [PMID: 37948388 PMCID: PMC10637695 DOI: 10.1371/journal.pone.0294050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 10/24/2023] [Indexed: 11/12/2023] Open
Abstract
The present study sought to leverage machine learning approaches to determine whether social determinants of health improve prediction of incident cardiovascular disease (CVD). Participants in the Jackson Heart study with no history of CVD at baseline were followed over a 10-year period to determine first CVD events (i.e., coronary heart disease, stroke, heart failure). Three modeling algorithms (i.e., Deep Neural Network, Random Survival Forest, Penalized Cox Proportional Hazards) were used to evaluate three feature sets (i.e., demographics and standard/biobehavioral CVD risk factors [FS1], FS1 combined with psychosocial and socioeconomic CVD risk factors [FS2], and FS2 combined with environmental features [FS3]) as predictors of 10-year CVD risk. Contrary to hypothesis, overall predictive accuracy did not improve when adding social determinants of health. However, social determinants of health comprised eight of the top 15 predictors of first CVD events. The social determinates of health indicators included four socioeconomic factors (insurance status and types), one psychosocial factor (discrimination burden), and three environmental factors (density of outdoor physical activity resources, including instructional and water activities; modified retail food environment index excluding alcohol; and favorable food stores). Findings suggest that whereas understanding biological determinants may identify who is currently at risk for developing CVD and in need of secondary prevention, understanding upstream social determinants of CVD risk could guide primary prevention efforts by identifying where and how policy and community-level interventions could be targeted to facilitate changes in individual health behaviors.
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Affiliation(s)
- Matthew C. Morris
- Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
- Department of Psychiatry and Human Behavior, University of Mississippi Medical Center, Jackson, Mississippi, United States of America
| | - Hamidreza Moradi
- Department of Data Science, University of Mississippi Medical Center, Jackson, Mississippi, United States of America
- Department of Computer Science, University of North Carolina Agricultural and Technical State University, Greensboro, North Carolina, United States of America
| | - Maryam Aslani
- Department of Data Analytics, University of North Texas, Denton, Texas, United States of America
| | - Mario Sims
- Department of Social Medicine, Population, and Public Health, University of California, Riverside, California, United States of America
| | - David Schlundt
- Department of Psychology, Vanderbilt University, Nashville, Tennessee, United States of America
| | - Chrystyna D. Kouros
- Department of Psychology, Southern Methodist University, Dallas, Texas, United States of America
| | - Burel Goodin
- Department of Psychology, University of Alabama at Birmingham, Birmingham, Alabama, Texas, United States of America
- Department of Anesthesiology, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Crystal Lim
- Department of Health Psychology, University of Missouri, Columbia, Missouri, Texas, United States of America
| | - Kerry Kinney
- Department of Psychology, Vanderbilt University, Nashville, Tennessee, United States of America
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Ba R, Durand A, Mauduit V, Chauveau C, Le Bas-Bernardet S, Salle S, Guérif P, Morin M, Petit C, Douillard V, Rousseau O, Blancho G, Kerleau C, Vince N, Giral M, Gourraud PA, Limou S. KiT-GENIE, the French genetic biobank of kidney transplantation. Eur J Hum Genet 2023; 31:1291-1299. [PMID: 36737541 PMCID: PMC10620190 DOI: 10.1038/s41431-023-01294-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 12/16/2022] [Accepted: 01/16/2023] [Indexed: 02/05/2023] Open
Abstract
KiT-GENIE is a monocentric DNA biobank set up to consolidate the very rich and homogeneous DIVAT French cohort of kidney donors and recipients (D/R) in order to explore the molecular factors involved in kidney transplantation outcomes. We collected DNA samples for kidney transplantations performed in Nantes, and we leveraged GWAS genotyping data for securing high-quality genetic data with deep SNP and HLA annotations through imputations and for inferring D/R genetic ancestry. Overall, the biobank included 4217 individuals (n = 1945 D + 2,272 R, including 1969 D/R pairs), 7.4 M SNPs and over 200 clinical variables. KiT-GENIE represents an accurate snapshot of kidney transplantation clinical practice in Nantes between 2002 and 2018, with an enrichment in living kidney donors (17%) and recipients with focal segmental glomerulosclerosis (4%). Recipients were predominantly male (63%), of European ancestry (93%), with a mean age of 51yo and 86% experienced their first graft over the study period. D/R pairs were 93% from European ancestry, and 95% pairs exhibited at least one HLA allelic mismatch. The mean follow-up time was 6.7 years with a hindsight up to 25 years. Recipients experienced biopsy-proven rejection and graft loss for 16.6% and 21.3%, respectively. KiT-GENIE constitutes one of the largest kidney transplantation genetic cohorts worldwide to date. It includes homogeneous high-quality clinical and genetic data for donors and recipients, hence offering a unique opportunity to investigate immunogenetic and genetic factors, as well as donor-recipient interactions and mismatches involved in rejection, graft survival, primary disease recurrence and other comorbidities.
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Affiliation(s)
- Rokhaya Ba
- Nantes Université, Centrale Nantes, CHU Nantes, Inserm, Centre de Recherche Translationnelle en Transplantation et Immunologie, UMR 1064, F-44000, Nantes, France
| | - Axelle Durand
- Nantes Université, Centrale Nantes, CHU Nantes, Inserm, Centre de Recherche Translationnelle en Transplantation et Immunologie, UMR 1064, F-44000, Nantes, France
| | - Vincent Mauduit
- Nantes Université, Centrale Nantes, CHU Nantes, Inserm, Centre de Recherche Translationnelle en Transplantation et Immunologie, UMR 1064, F-44000, Nantes, France
| | - Christine Chauveau
- Nantes Université, Centrale Nantes, CHU Nantes, Inserm, Centre de Recherche Translationnelle en Transplantation et Immunologie, UMR 1064, F-44000, Nantes, France
| | - Stéphanie Le Bas-Bernardet
- Nantes Université, Centrale Nantes, CHU Nantes, Inserm, Centre de Recherche Translationnelle en Transplantation et Immunologie, UMR 1064, F-44000, Nantes, France
| | - Sonia Salle
- Nantes Université, Centrale Nantes, CHU Nantes, Inserm, Centre de Recherche Translationnelle en Transplantation et Immunologie, UMR 1064, F-44000, Nantes, France
| | - Pierrick Guérif
- CHU Nantes, Nantes Université, Service de Néphrologie-Immunologie Clinique, ITUN, F-44000, Nantes, France
| | - Martin Morin
- Nantes Université, Centrale Nantes, CHU Nantes, Inserm, Centre de Recherche Translationnelle en Transplantation et Immunologie, UMR 1064, F-44000, Nantes, France
| | - Clémence Petit
- Nantes Université, Centrale Nantes, CHU Nantes, Inserm, Centre de Recherche Translationnelle en Transplantation et Immunologie, UMR 1064, F-44000, Nantes, France
- CHU Nantes, Nantes Université, Service de Néphrologie-Immunologie Clinique, ITUN, F-44000, Nantes, France
| | - Venceslas Douillard
- Nantes Université, Centrale Nantes, CHU Nantes, Inserm, Centre de Recherche Translationnelle en Transplantation et Immunologie, UMR 1064, F-44000, Nantes, France
| | - Olivia Rousseau
- Nantes Université, Centrale Nantes, CHU Nantes, Inserm, Centre de Recherche Translationnelle en Transplantation et Immunologie, UMR 1064, F-44000, Nantes, France
| | - Gilles Blancho
- Nantes Université, Centrale Nantes, CHU Nantes, Inserm, Centre de Recherche Translationnelle en Transplantation et Immunologie, UMR 1064, F-44000, Nantes, France
- CHU Nantes, Nantes Université, Service de Néphrologie-Immunologie Clinique, ITUN, F-44000, Nantes, France
| | - Clarisse Kerleau
- CHU Nantes, Nantes Université, Service de Néphrologie-Immunologie Clinique, ITUN, F-44000, Nantes, France
| | - Nicolas Vince
- Nantes Université, Centrale Nantes, CHU Nantes, Inserm, Centre de Recherche Translationnelle en Transplantation et Immunologie, UMR 1064, F-44000, Nantes, France
| | - Magali Giral
- Nantes Université, Centrale Nantes, CHU Nantes, Inserm, Centre de Recherche Translationnelle en Transplantation et Immunologie, UMR 1064, F-44000, Nantes, France
- CHU Nantes, Nantes Université, Service de Néphrologie-Immunologie Clinique, ITUN, F-44000, Nantes, France
| | - Pierre-Antoine Gourraud
- Nantes Université, Centrale Nantes, CHU Nantes, Inserm, Centre de Recherche Translationnelle en Transplantation et Immunologie, UMR 1064, F-44000, Nantes, France
| | - Sophie Limou
- Nantes Université, Centrale Nantes, CHU Nantes, Inserm, Centre de Recherche Translationnelle en Transplantation et Immunologie, UMR 1064, F-44000, Nantes, France.
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Hsiao YC, Kuo CY, Lin FJ, Wu YW, Lin TH, Yeh HI, Chen JW, Wu CC. Machine Learning Models for ASCVD Risk Prediction in an Asian Population - How to Validate the Model is Important. ACTA CARDIOLOGICA SINICA 2023; 39:901-912. [PMID: 38022427 PMCID: PMC10646597 DOI: 10.6515/acs.202311_39(6).20230528a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 05/28/2023] [Indexed: 12/01/2023]
Abstract
Introduction Atherosclerotic cardiovascular disease (ASCVD) is prevalent worldwide including Taiwan, however widely accepted tools to assess the risk of ASCVD are lacking in Taiwan. Machine learning models are potentially useful for risk evaluation. In this study we used two cohorts to test the feasibility of machine learning with transfer learning for developing an ASCVD risk prediction model in Taiwan. Methods Two multi-center observational registry cohorts, T-SPARCLE and T-PPARCLE were used in this study. The variables selected were based on European, U.S. and Asian guidelines. Both registries recorded the ASCVD outcomes of the patients. Ten-fold validation and temporal validation methods were used to evaluate the performance of the binary classification analysis [prediction of major adverse cardiovascular (CV) events in one year]. Time-to-event analyses were also performed. Results In the binary classification analysis, eXtreme Gradient Boosting (XGBoost) and random forest had the best performance, with areas under the receiver operating characteristic curve (AUC-ROC) of 0.72 (0.68-0.76) and 0.73 (0.69-0.77), respectively, although it was not significantly better than other models. Temporal validation was also performed, and the data showed significant differences in the distribution of various features and event rate. The AUC-ROC of XGBoost dropped to 0.66 (0.59-0.73), while that of random forest dropped to 0.69 (0.62-0.76) in the temporal validation method, and the performance also became numerically worse than that of the logistic regression model. In the time-to-event analysis, most models had a concordance index of around 0.70. Conclusions Machine learning models with appropriate transfer learning may be a useful tool for the development of CV risk prediction models and may help improve patient care in the future.
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Affiliation(s)
- Yu-Chung Hsiao
- Department of Internal Medicine, National Taiwan University Hospital
| | - Chen-Yuan Kuo
- Center for Healthy Longevity and Aging Sciences, National Yang Ming Chiao Tung University
| | - Fang-Ju Lin
- Graduate Institute of Clinical Pharmacy & School of Pharmacy, College of Medicine, National Taiwan University
- Department of Pharmacy, National Taiwan University Hospital, Taipei
| | - Yen-Wen Wu
- Division of Cardiology, Cardiovascular Medical Center, Far Eastern Memorial Hospital, New Taipei City
- School of Medicine, National Yang Ming Chiao Tung University, School of Medicine, Taipei
- Graduate Institute of Medicine, Yuan Ze University, Taoyuan
| | - Tsung-Hsien Lin
- Division of Cardiology, Department of Internal Medicine, Kaohsiung Medical University Hospital
- Faculty of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung
| | - Hung-I Yeh
- MacKay Memorial Hospital, MacKay Medical College
| | - Jaw-Wen Chen
- Department of Medical Research and Education, Taipei Veterans General Hospital
| | - Chau-Chung Wu
- Department of Internal Medicine, National Taiwan University Hospital
- Graduate Institute of Medical Education & Bioethics, College of Medicine, National Taiwan University, Taipei, Taiwan
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Atehortúa A, Gkontra P, Camacho M, Diaz O, Bulgheroni M, Simonetti V, Chadeau-Hyam M, Felix JF, Sebert S, Lekadir K. Cardiometabolic risk estimation using exposome data and machine learning. Int J Med Inform 2023; 179:105209. [PMID: 37729839 DOI: 10.1016/j.ijmedinf.2023.105209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 08/11/2023] [Accepted: 08/30/2023] [Indexed: 09/22/2023]
Abstract
BACKGROUND The human exposome encompasses all exposures that individuals encounter throughout their lifetime. It is now widely acknowledged that health outcomes are influenced not only by genetic factors but also by the interactions between these factors and various exposures. Consequently, the exposome has emerged as a significant contributor to the overall risk of developing major diseases, such as cardiovascular disease (CVD) and diabetes. Therefore, personalized early risk assessment based on exposome attributes might be a promising tool for identifying high-risk individuals and improving disease prevention. OBJECTIVE Develop and evaluate a novel and fair machine learning (ML) model for CVD and type 2 diabetes (T2D) risk prediction based on a set of readily available exposome factors. We evaluated our model using internal and external validation groups from a multi-center cohort. To be considered fair, the model was required to demonstrate consistent performance across different sub-groups of the cohort. METHODS From the UK Biobank, we identified 5,348 and 1,534 participants who within 13 years from the baseline visit were diagnosed with CVD and T2D, respectively. An equal number of participants who did not develop these pathologies were randomly selected as the control group. 109 readily available exposure variables from six different categories (physical measures, environmental, lifestyle, mental health events, sociodemographics, and early-life factors) from the participant's baseline visit were considered. We adopted the XGBoost ensemble model to predict individuals at risk of developing the diseases. The model's performance was compared to that of an integrative ML model which is based on a set of biological, clinical, physical, and sociodemographic variables, and, additionally for CVD, to the Framingham risk score. Moreover, we assessed the proposed model for potential bias related to sex, ethnicity, and age. Lastly, we interpreted the model's results using SHAP, a state-of-the-art explainability method. RESULTS The proposed ML model presents a comparable performance to the integrative ML model despite using solely exposome information, achieving a ROC-AUC of 0.78±0.01 and 0.77±0.01 for CVD and T2D, respectively. Additionally, for CVD risk prediction, the exposome-based model presents an improved performance over the traditional Framingham risk score. No bias in terms of key sensitive variables was identified. CONCLUSIONS We identified exposome factors that play an important role in identifying patients at risk of CVD and T2D, such as naps during the day, age completed full-time education, past tobacco smoking, frequency of tiredness/unenthusiasm, and current work status. Overall, this work demonstrates the potential of exposome-based machine learning as a fair CVD and T2D risk assessment tool.
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Affiliation(s)
- Angélica Atehortúa
- BCN-AIM laboratory, Facultat de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain.
| | - Polyxeni Gkontra
- BCN-AIM laboratory, Facultat de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Marina Camacho
- BCN-AIM laboratory, Facultat de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Oliver Diaz
- BCN-AIM laboratory, Facultat de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain
| | | | | | - Marc Chadeau-Hyam
- Department of Epidemiology and Biostatistics, MRC-HPA Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom
| | - Janine F Felix
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands; Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Sylvain Sebert
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Karim Lekadir
- BCN-AIM laboratory, Facultat de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain
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Omar I, Khan M, Starr A, Abou Rok Ba K. Automated Prediction of Crack Propagation Using H2O AutoML. SENSORS (BASEL, SWITZERLAND) 2023; 23:8419. [PMID: 37896512 PMCID: PMC10611134 DOI: 10.3390/s23208419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 10/06/2023] [Accepted: 10/09/2023] [Indexed: 10/29/2023]
Abstract
Crack propagation is a critical phenomenon in materials science and engineering, significantly impacting structural integrity, reliability, and safety across various applications. The accurate prediction of crack propagation behavior is paramount for ensuring the performance and durability of engineering components, as extensively explored in prior research. Nevertheless, there is a pressing demand for automated models capable of efficiently and precisely forecasting crack propagation. In this study, we address this need by developing a machine learning-based automated model using the powerful H2O library. This model aims to accurately predict crack propagation behavior in various materials by analyzing intricate crack patterns and delivering reliable predictions. To achieve this, we employed a comprehensive dataset derived from measured instances of crack propagation in Acrylonitrile Butadiene Styrene (ABS) specimens. Rigorous evaluation metrics, including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared (R2) values, were applied to assess the model's predictive accuracy. Cross-validation techniques were utilized to ensure its robustness and generalizability across diverse datasets. Our results underscore the automated model's remarkable accuracy and reliability in predicting crack propagation. This study not only highlights the immense potential of the H2O library as a valuable tool for structural health monitoring but also advocates for the broader adoption of Automated Machine Learning (AutoML) solutions in engineering applications. In addition to presenting these findings, we define H2O as a powerful machine learning library and AutoML as Automated Machine Learning to ensure clarity and understanding for readers unfamiliar with these terms. This research not only demonstrates the significance of AutoML in future-proofing our approach to structural integrity and safety but also emphasizes the need for comprehensive reporting and understanding in scientific discourse.
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Affiliation(s)
| | - Muhammad Khan
- School of Aerospace, Transport and Manufacturing, Cranfield University, Bedford MK43 0AL, UK
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Menteş N, Çakmak MA, Kurt ME. Estimation of service length with the machine learning algorithms and neural networks for patients who receiving home health care. EVALUATION AND PROGRAM PLANNING 2023; 100:102324. [PMID: 37290209 DOI: 10.1016/j.evalprogplan.2023.102324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 05/26/2023] [Accepted: 05/26/2023] [Indexed: 06/10/2023]
Abstract
The main purpose of the study is to develop an estimation model using machine learning algorithms and to ensure the effective and efficient implementation of home health care service planning in hospitals with these algorithms. The necessary approvals for the study were obtained. The data set was created by obtaining patient data (except for data such as Turkish Republic identification number) from 14 hospitals providing Home Health Care Services in the city of Diyarbakır. The data set was subjected to necessary pre-processing and descriptive statistics were applied. For the estimation model, Decision Tree, Random Forest and Multi-layer Perceptron Neural Network algorithms were used. It was found that the number of days of home health care service, which the patients received, varied depending on their age and gender. It was observed that the patients were generally in the disease groups that required Physiotherapy and Rehabilitation treatments. It was determined that the length of service for patients can be predicted with a high reliability rate (Multi-Layer Model Acc: 90.4%, Decision Tree Model Acc: 86.4%, Random Forest Model Acc: 88.5%) using machine learning algorithms. In the light of the findings and data patterns obtained in the study, it is thought that effective and efficient planning will be made in terms of health management. In addition, it is believed that estimating the average length of service for patients will contribute to strategic planning of human resources for health, and to reducing medical consumables, drugs and hospital expenses.
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Affiliation(s)
- Nurettin Menteş
- Inonu University, Malatya Vocational School, Malatya, Turkey.
| | - Mehmet Aziz Çakmak
- Dicle University, Faculty of Economics and Administrative Sciences, Department of Health Management, Diyarbakır, Turkey.
| | - Mehmet Emin Kurt
- Dicle University, Faculty of Economics and Administrative Sciences, Department of Health Management, Diyarbakır, Turkey.
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Mohd Faizal AS, Hon WY, Thevarajah TM, Khor SM, Chang SW. A biomarker discovery of acute myocardial infarction using feature selection and machine learning. Med Biol Eng Comput 2023; 61:2527-2541. [PMID: 37199891 PMCID: PMC10191821 DOI: 10.1007/s11517-023-02841-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 04/25/2023] [Indexed: 05/19/2023]
Abstract
Acute myocardial infarction (AMI) or heart attack is a significant global health threat and one of the leading causes of death. The evolution of machine learning has greatly revamped the risk stratification and death prediction of AMI. In this study, an integrated feature selection and machine learning approach was used to identify potential biomarkers for early detection and treatment of AMI. First, feature selection was conducted and evaluated before all classification tasks with machine learning. Full classification models (using all 62 features) and reduced classification models (using various feature selection methods ranging from 5 to 30 features) were built and evaluated using six machine learning classification algorithms. The results showed that the reduced models performed generally better (mean AUPRC via random forest (RF) algorithm for recursive feature elimination (RFE) method ranges from 0.8048 to 0.8260, while for random forest importance (RFI) method, it ranges from 0.8301 to 0.8505) than the full models (mean AUPRC via RF: 0.8044). The most notable finding of this study was the identification of a five-feature model that included cardiac troponin I, HDL cholesterol, HbA1c, anion gap, and albumin, which had achieved comparable results (mean AUPRC via RF: 0.8462) as to the models that containing more features. These five features were proven by the previous studies as significant risk factors for AMI or cardiovascular disease and could be used as potential biomarkers to predict the prognosis of AMI patients. From the medical point of view, fewer features for diagnosis or prognosis could reduce the cost and time of a patient as lesser clinical and pathological tests are needed.
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Affiliation(s)
- Aizatul Shafiqah Mohd Faizal
- Bioinformatics Program, Institute of Biological Sciences, Faculty of Science, Universiti Malaya, 50603, Kuala Lumpur, Malaysia
| | - Wei Yin Hon
- Bioinformatics Program, Institute of Biological Sciences, Faculty of Science, Universiti Malaya, 50603, Kuala Lumpur, Malaysia
| | - T Malathi Thevarajah
- Department of Pathology, Faculty of Medicine, Universiti Malaya, 50603, Kuala Lumpur, Malaysia
| | - Sook Mei Khor
- Department of Chemistry, Faculty of Science, Universiti Malaya, 50603, Kuala Lumpur, Malaysia
| | - Siow-Wee Chang
- Bioinformatics Program, Institute of Biological Sciences, Faculty of Science, Universiti Malaya, 50603, Kuala Lumpur, Malaysia.
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Stabellini N, Cullen J, Moore JX, Dent S, Sutton AL, Shanahan J, Montero AJ, Guha A. Social Determinants of Health Data Improve the Prediction of Cardiac Outcomes in Females with Breast Cancer. Cancers (Basel) 2023; 15:4630. [PMID: 37760599 PMCID: PMC10526347 DOI: 10.3390/cancers15184630] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 09/08/2023] [Accepted: 09/14/2023] [Indexed: 09/29/2023] Open
Abstract
Cardiovascular disease is the leading cause of mortality among breast cancer (BC) patients aged 50 and above. Machine Learning (ML) models are increasingly utilized as prediction tools, and recent evidence suggests that incorporating social determinants of health (SDOH) data can enhance its performance. This study included females ≥ 18 years diagnosed with BC at any stage. The outcomes were the diagnosis and time-to-event of major adverse cardiovascular events (MACEs) within two years following a cancer diagnosis. Covariates encompassed demographics, risk factors, individual and neighborhood-level SDOH, tumor characteristics, and BC treatment. Race-specific and race-agnostic Extreme Gradient Boosting ML models with and without SDOH data were developed and compared based on their C-index. Among 4309 patients, 11.4% experienced a 2-year MACE. The race-agnostic models exhibited a C-index of 0.78 (95% CI 0.76-0.79) and 0.81 (95% CI 0.80-0.82) without and with SDOH data, respectively. In non-Hispanic Black women (NHB; n = 765), models without and with SDOH data achieved a C-index of 0.74 (95% CI 0.72-0.76) and 0.75 (95% CI 0.73-0.78), respectively. Among non-Hispanic White women (n = 3321), models without and with SDOH data yielded a C-index of 0.79 (95% CI 0.77-0.80) and 0.79 (95% CI 0.77-0.80), respectively. In summary, including SDOH data improves the predictive performance of ML models in forecasting 2-year MACE among BC females, particularly within NHB.
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Affiliation(s)
- Nickolas Stabellini
- Case Western Reserve University School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA
- Department of Hematology-Oncology, University Hospitals Seidman Cancer Center, Cleveland, OH 44106, USA
- Faculdade Israelita de Ciências da Saúde Albert Einstein, Hospital Israelita Albert Einstein, São Paulo 05652-900, SP, Brazil
- Department of Medicine, Medical College of Georgia, Augusta University, Augusta, GA 30912, USA;
| | - Jennifer Cullen
- Case Western Reserve University School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA
- Case Comprehensive Cancer Center, Cleveland, OH 44106, USA
| | - Justin X. Moore
- Center for Health Equity Transformation, Department of Behavioral Science, Department of Internal Medicine, Markey Cancer Center, University of Kentucky College of Medicine, Lexington, KY 40506, USA
| | - Susan Dent
- Duke Cancer Institute, Duke University, Durham, NC 27708, USA
| | - Arnethea L. Sutton
- Department of Kinesiology and Health Sciences, College of Humanities and Sciences, Virginia Commonwealth University, Richmond, VA 23284, USA
| | - John Shanahan
- Cancer Informatics, Seidman Cancer Center, University Hospitals of Cleveland, Cleveland, OH 44106, USA
| | - Alberto J. Montero
- Department of Hematology-Oncology, University Hospitals Seidman Cancer Center, Cleveland, OH 44106, USA
| | - Avirup Guha
- Department of Medicine, Medical College of Georgia, Augusta University, Augusta, GA 30912, USA;
- Cardio-Oncology Program, Medical College of Georgia, Augusta University, Augusta, GA 30912, USA
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Niazi SK. The Coming of Age of AI/ML in Drug Discovery, Development, Clinical Testing, and Manufacturing: The FDA Perspectives. Drug Des Devel Ther 2023; 17:2691-2725. [PMID: 37701048 PMCID: PMC10493153 DOI: 10.2147/dddt.s424991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 08/24/2023] [Indexed: 09/14/2023] Open
Abstract
Artificial intelligence (AI) and machine learning (ML) represent significant advancements in computing, building on technologies that humanity has developed over millions of years-from the abacus to quantum computers. These tools have reached a pivotal moment in their development. In 2021 alone, the U.S. Food and Drug Administration (FDA) received over 100 product registration submissions that heavily relied on AI/ML for applications such as monitoring and improving human performance in compiling dossiers. To ensure the safe and effective use of AI/ML in drug discovery and manufacturing, the FDA and numerous other U.S. federal agencies have issued continuously updated, stringent guidelines. Intriguingly, these guidelines are often generated or updated with the aid of AI/ML tools themselves. The overarching goal is to expedite drug discovery, enhance the safety profiles of existing drugs, introduce novel treatment modalities, and improve manufacturing compliance and robustness. Recent FDA publications offer an encouraging outlook on the potential of these tools, emphasizing the need for their careful deployment. This has expanded market opportunities for retraining personnel handling these technologies and enabled innovative applications in emerging therapies such as gene editing, CRISPR-Cas9, CAR-T cells, mRNA-based treatments, and personalized medicine. In summary, the maturation of AI/ML technologies is a testament to human ingenuity. Far from being autonomous entities, these are tools created by and for humans designed to solve complex problems now and in the future. This paper aims to present the status of these technologies, along with examples of their present and future applications.
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Cundrič L, Bosnić Z, Kaminsky LA, Myers J, Peterman JE, Markovic V, Arena R, Popović D. A Machine Learning Approach to Developing an Accurate Prediction of Maximal Heart Rate During Exercise Testing in Apparently Healthy Adults. J Cardiopulm Rehabil Prev 2023; 43:377-383. [PMID: 36880964 DOI: 10.1097/hcr.0000000000000786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/08/2023]
Abstract
PURPOSE Maximal heart rate (HR max ) continues to be an important measure of adequate effort during an exercise test. The aim of this study was to improve the accuracy of HR max prediction using a machine learning (ML) approach. METHODS We used a sample from the Fitness Registry of the Importance of Exercise National Database, which included 17 325 apparently healthy individuals (81% males) who performed a maximal cardiopulmonary exercise test. Two standard formulas for HR max prediction were tested: Formula1 = 220 - age (yr), root-mean-squared error (RMSE) 21.9, relative root-mean-squared error (RRMSE) 1.1; and Formula2 = 209.3 - 0.72 × age (yr), RMSE 22.7 and RRMSE 1.1. For ML model prediction, we used age, weight, height, resting HR, and systolic and diastolic blood pressure. The following ML algorithms to predict HR max were applied: lasso regression (LR), neural networks (NN), support vector machine (SVM) and random forests (RF). An evaluation was performed using cross-validation and by computing the RMSE and RRMSE, Pearson correlation, and Bland-Altman plots. The best predictive model was explained with Shapley Additive Explanations (SHAP). RESULTS The HR max for the cohort was 162 ± 20 bpm. All ML models improved HR max prediction and reduced RMSE and RRMSE compared with Formula1 (LR: 20.2%, NN: 20.4%, SVM: 22.2%, and RF: 24.7%). The predictions of all algorithms significantly correlated with HR max ( r = 0.49, 0.51, 0.54, 0.57, respectively; P < .001). Bland-Altman analysis demonstrated lower bias and 95% CI for all ML models in comparison with standard equations. The SHAP explanation showed a high impact of all selected variables. CONCLUSIONS Machine learning, particularly the RF model, improved prediction of HR max using readily available measures. This approach should be considered for clinical application to refine HR max prediction.
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Affiliation(s)
- Larsen Cundrič
- University of Ljubljana, Faculty of Computer and Information Science, Ljubljana, Slovenia (Mr Cundrič and Dr Bosnić); Fisher Institute of Health and Well-Being and Clinical Exercise Physiology Laboratory, Ball State University, Muncie, Indiana (Drs Kaminsky and Peterman); VA Palo Alto Health Care System and Stanford University, Palo Alto, California (Dr Myers); Departments of Information Systems, Faculty of Organizational Sciences (Dr Markovic) and Physiology, Faculty of Pharmacy (Dr Popović), University of Belgrade, Belgrade, Serbia; Department of Physical Therapy, College of Applied Science, University of Illinois at Chicago (Dr Arena); Division of Cardiology, University Clinical Center of Serbia, Belgrade, Serbia (Dr Popović); and Department for Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota (Dr Popović)
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Kim Y, Kim J, Kim S, Youn H, Choi J, Seo K. Machine learning-based risk prediction model for canine myxomatous mitral valve disease using electronic health record data. Front Vet Sci 2023; 10:1189157. [PMID: 37720471 PMCID: PMC10500836 DOI: 10.3389/fvets.2023.1189157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Accepted: 08/15/2023] [Indexed: 09/19/2023] Open
Abstract
Introduction Myxomatous mitral valve disease (MMVD) is the most common cause of heart failure in dogs, and assessing the risk of heart failure in dogs with MMVD is often challenging. Machine learning applied to electronic health records (EHRs) is an effective tool for predicting prognosis in the medical field. This study aimed to develop machine learning-based heart failure risk prediction models for dogs with MMVD using a dataset of EHRs. Methods A total of 143 dogs with MMVD between May 2018 and May 2022. Complete medical records were reviewed for all patients. Demographic data, radiographic measurements, echocardiographic values, and laboratory results were obtained from the clinical database. Four machine-learning algorithms (random forest, K-nearest neighbors, naïve Bayes, support vector machine) were used to develop risk prediction models. Model performance was represented by plotting the receiver operating characteristic (ROC) curve and calculating the area under the curve (AUC). The best-performing model was chosen for the feature-ranking process. Results The random forest model showed superior performance to the other models (AUC = 0.88), while the performance of the K-nearest neighbors model showed the lowest performance (AUC = 0.69). The top three models showed excellent performance (AUC ≥ 0.8). According to the random forest algorithm's feature ranking, echocardiographic and radiographic variables had the highest predictive values for heart failure, followed by packed cell volume (PCV) and respiratory rates. Among the electrolyte variables, chloride had the highest predictive value for heart failure. Discussion These machine-learning models will enable clinicians to support decision-making in estimating the prognosis of patients with MMVD.
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Affiliation(s)
- Yunji Kim
- Department of Veterinary Internal Medicine, College of Veterinary Medicine, Seoul, Republic of Korea
| | - Jaejin Kim
- School of Biological Sciences, Seoul National University, Seoul, Republic of Korea
| | - Sehoon Kim
- Department of Veterinary Internal Medicine, College of Veterinary Medicine, Seoul, Republic of Korea
| | - Hwayoung Youn
- Department of Veterinary Internal Medicine, College of Veterinary Medicine, Seoul, Republic of Korea
| | - Jihye Choi
- Department of Veterinary Medical Imaging, College of Veterinary Medicine, Seoul National University, Seoul, Republic of Korea
| | - Kyoungwon Seo
- Department of Veterinary Internal Medicine, College of Veterinary Medicine, Seoul, Republic of Korea
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Huque MH, Kootar S, Eramudugolla R, Han SD, Carlson MC, Lopez OL, Bennett DA, Peters R, Anstey KJ. CogDrisk, ANU-ADRI, CAIDE, and LIBRA Risk Scores for Estimating Dementia Risk. JAMA Netw Open 2023; 6:e2331460. [PMID: 37647064 PMCID: PMC10469268 DOI: 10.1001/jamanetworkopen.2023.31460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 07/21/2023] [Indexed: 09/01/2023] Open
Abstract
Importance While the Australian National University-Alzheimer Disease Risk Index (ANU-ADRI), Cardiovascular Risk Factors, Aging, and Dementia (CAIDE), and Lifestyle for Brain Health (LIBRA) dementia risk tools have been widely used, a large body of new evidence has emerged since their publication. Recently, Cognitive Health and Dementia Risk Index (CogDrisk) and CogDrisk for Alzheimer disease (CogDrisk-AD) risk tools have been developed for the assessment of dementia and AD risk, respectively, using contemporary evidence; comparison of the relative performance of these risk tools is limited. Objective To evaluate the performance of CogDrisk, ANU-ADRI, CAIDE, LIBRA, and modified LIBRA (LIBRA with age and sex estimates from ANU-ADRI) in estimating dementia and AD risks (with CogDrisk-AD and ANU-ADRI). Design, Setting, and Participants This population-based cohort study obtained data from the Rush Memory and Aging Project (MAP), the Cardiovascular Health Study Cognition Study (CHS-CS), and the Health and Retirement Study-Aging, Demographics and Memory Study (HRS-ADAMS). Participants who were free of dementia at baseline were included. The factors were component variables in the risk tools that included self-reported baseline demographics, medical risk factors, and lifestyle habits. The study was conducted between November 2021 and March 2023, and statistical analysis was performed from January to June 2023. Main outcomes and measures Risk scores were calculated based on available factors in each of these cohorts. Area under the receiver operating characteristic curve (AUC) was calculated to measure the performance of each risk score. Multiple imputation was used to assess whether missing data may have affected estimates for dementia risk. Results Among the 6107 participants in 3 validation cohorts included for this study, 2184 participants without dementia at baseline were available from MAP (mean [SD] age, 80.0 [7.6] years; 1606 [73.5%] female), 548 participants without dementia at baseline were available from HRS-ADAMS (mean [SD] age, 79.5 [6.3] years; 288 [52.5%] female), and 3375 participants without dementia at baseline were available from CHS-CS (mean [SD] age, 74.8 [4.9] years; 1994 [59.1%] female). In all 3 cohorts, a similar AUC for dementia was obtained using CogDrisk, ANU-ADRI, and modified LIBRA (MAP cohort: CogDrisk AUC, 0.65 [95% CI, 0.61-0.69]; ANU-ADRI AUC, 0.65 [95% CI, 0.61-0.69]; modified LIBRA AUC, 0.65 [95% CI, 0.61-0.69]; HRS-ADAMS cohort: CogDrisk AUC, 0.75 [95% CI, 0.71-0.79]; ANU-ADRI AUC, 0.74 [95% CI, 0.70-0.78]; modified LIBRA AUC, 0.75 [95% CI, 0.71-0.79]; CHS-CS cohort: CogDrisk AUC, 0.70 [95% CI, 0.67-0.72]; ANU-ADRI AUC, 0.69 [95% CI, 0.66-0.72]; modified LIBRA AUC, 0.70 [95% CI, 0.68-0.73]). The CAIDE and LIBRA also provided similar but lower AUCs than the 3 aforementioned tools (eg, MAP cohort: CAIDE AUC, 0.50 [95% CI, 0.46-0.54]; LIBRA AUC, 0.53 [95% CI, 0.48-0.57]). The performance of CogDrisk-AD and ANU-ADRI in estimating AD risks was also similar. Conclusions and relevance CogDrisk and CogDrisk-AD performed similarly to ANU-ADRI in estimating dementia and AD risks. These results suggest that CogDrisk and CogDrisk-AD, with a greater range of modifiable risk factors compared with other risk tools in this study, may be more informative for risk reduction.
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Affiliation(s)
- Md Hamidul Huque
- School of Psychology, University of New South Wales, Kensington, New South Wales, Australia
- Neuroscience Research Australia, Randwick, New South Wales, Australia
- University of New South Wales Ageing Futures Institute, University of New South Wales, Kensington, New South Wales, Australia
| | - Scherazad Kootar
- School of Psychology, University of New South Wales, Kensington, New South Wales, Australia
- Neuroscience Research Australia, Randwick, New South Wales, Australia
- University of New South Wales Ageing Futures Institute, University of New South Wales, Kensington, New South Wales, Australia
| | - Ranmalee Eramudugolla
- School of Psychology, University of New South Wales, Kensington, New South Wales, Australia
- Neuroscience Research Australia, Randwick, New South Wales, Australia
- University of New South Wales Ageing Futures Institute, University of New South Wales, Kensington, New South Wales, Australia
| | - S. Duke Han
- Department of Family Medicine, Keck School of Medicine of the University of Southern California, Los Angeles
| | - Michelle C. Carlson
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, and Johns Hopkins Center on Aging and Health, Baltimore, Maryland
| | - Oscar L. Lopez
- Departments of Neurology and Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - David A. Bennett
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, Illinois
| | - Ruth Peters
- The George Institute of Global Health, University of New South Wales, Kensington, New South Wales, Australia
| | - Kaarin J. Anstey
- School of Psychology, University of New South Wales, Kensington, New South Wales, Australia
- Neuroscience Research Australia, Randwick, New South Wales, Australia
- University of New South Wales Ageing Futures Institute, University of New South Wales, Kensington, New South Wales, Australia
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Mamouei M, Fisher T, Rao S, Li Y, Salimi-Khorshidi G, Rahimi K. A comparative study of model-centric and data-centric approaches in the development of cardiovascular disease risk prediction models in the UK Biobank. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2023; 4:337-346. [PMID: 37538143 PMCID: PMC10393888 DOI: 10.1093/ehjdh/ztad033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 04/01/2023] [Indexed: 08/05/2023]
Abstract
Aims A diverse set of factors influence cardiovascular diseases (CVDs), but a systematic investigation of the interplay between these determinants and the contribution of each to CVD incidence prediction is largely missing from the literature. In this study, we leverage one of the most comprehensive biobanks worldwide, the UK Biobank, to investigate the contribution of different risk factor categories to more accurate incidence predictions in the overall population, by sex, different age groups, and ethnicity. Methods and results The investigated categories include the history of medical events, behavioural factors, socioeconomic factors, environmental factors, and measurements. We included data from a cohort of 405 257 participants aged 37-73 years and trained various machine learning and deep learning models on different subsets of risk factors to predict CVD incidence. Each of the models was trained on the complete set of predictors and subsets where each category was excluded. The results were benchmarked against QRISK3. The findings highlight that (i) leveraging a more comprehensive medical history substantially improves model performance. Relative to QRISK3, the best performing models improved the discrimination by 3.78% and improved precision by 1.80%. (ii) Both model- and data-centric approaches are necessary to improve predictive performance. The benefits of using a comprehensive history of diseases were far more pronounced when a neural sequence model, BEHRT, was used. This highlights the importance of the temporality of medical events that existing clinical risk models fail to capture. (iii) Besides the history of diseases, socioeconomic factors and measurements had small but significant independent contributions to the predictive performance. Conclusion These findings emphasize the need for considering broad determinants and novel modelling approaches to enhance CVD incidence prediction.
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Affiliation(s)
- Mohammad Mamouei
- Corresponding author. Tel: +44 1865 617200, Fax: +44 1865 617202,
| | - Thomas Fisher
- Deep Medicine, Oxford Martin School, University of Oxford, 1st Floor, Hayes House, 75 George Street, Oxford OX1 2BQ, UK
- Nuffield Department of Women’s and Reproductive Health, Medical Science Division, University of Oxford, Oxford, UK
| | - Shishir Rao
- Deep Medicine, Oxford Martin School, University of Oxford, 1st Floor, Hayes House, 75 George Street, Oxford OX1 2BQ, UK
- Nuffield Department of Women’s and Reproductive Health, Medical Science Division, University of Oxford, Oxford, UK
| | - Yikuan Li
- Deep Medicine, Oxford Martin School, University of Oxford, 1st Floor, Hayes House, 75 George Street, Oxford OX1 2BQ, UK
- Nuffield Department of Women’s and Reproductive Health, Medical Science Division, University of Oxford, Oxford, UK
| | - Ghomalreza Salimi-Khorshidi
- Deep Medicine, Oxford Martin School, University of Oxford, 1st Floor, Hayes House, 75 George Street, Oxford OX1 2BQ, UK
- Nuffield Department of Women’s and Reproductive Health, Medical Science Division, University of Oxford, Oxford, UK
| | - Kazem Rahimi
- Deep Medicine, Oxford Martin School, University of Oxford, 1st Floor, Hayes House, 75 George Street, Oxford OX1 2BQ, UK
- Nuffield Department of Women’s and Reproductive Health, Medical Science Division, University of Oxford, Oxford, UK
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
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Huang Q, Zeng T, Zhang J, Min J, Zheng J, Tian S, Huang H, Liu X, Zhang H, Wang P, Hu X, Chen L. Framingham risk score conventional risk factors are potent to predict all-cause mortality using machine learning algorithms: a population-based prospective cohort study over 40 years in China. J Investig Med 2023; 71:586-590. [PMID: 37144834 DOI: 10.1177/10815589231169689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
Predicting all-cause mortality using available or conveniently modifiable risk factors is potentially crucial in reducing deaths precisely and efficiently. Framingham risk score (FRS) is widely used in predicting cardiovascular diseases, and its conventional risk factors are closely pertinent to deaths. Machine learning is increasingly considered to improve the predicting performances by developing predictive models. We aimed to develop the all-cause mortality predictive models using five machine learning (ML) algorithms (decision trees, random forest, support vector machine (SVM), XgBoost, and logistic regression) and determine whether FRS conventional risk factors are sufficient for predicting all-cause mortality in individuals over 40 years. Our data were obtained from a 10-year population-based prospective cohort study in China, including 9143 individuals over 40 years in 2011, and 6879 individuals followed-up in 2021. The all-cause mortality prediction models were developed using five ML algorithms by introducing all features available (182 items) or FRS conventional risk factors. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of the predictive models. The AUC and 95% confidence interval of the all-cause mortality prediction models developed by FRS conventional risk factors using five ML algorithms were 0.75 (0.726-0.772), 0.78 (0.755-0.799), 0.75 (0.731-0.777), 0.77 (0.747-0.792), and 0.78 (0.754-0.798), respectively, which is close to the AUC values of models established by all features (0.79 (0.769-0.812), 0.83 (0.807-0.848), 0.78 (0.753-0.798), 0.82 (0.796-0.838), and 0.85 (0.826-0.866), respectively). Therefore, we tentatively put forward that FRS conventional risk factors were potent to predict all-cause mortality using machine learning algorithms in the population over 40 years.
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Affiliation(s)
- QianQian Huang
- Union Hospital, Department of Endocrinology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - TianShu Zeng
- Union Hospital, Department of Endocrinology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - JiaoYue Zhang
- Union Hospital, Department of Endocrinology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Jie Min
- Union Hospital, Department of Endocrinology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Juan Zheng
- Union Hospital, Department of Endocrinology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - ShengHua Tian
- Union Hospital, Department of Endocrinology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | | | - XiaoHuan Liu
- Union Hospital, Department of Endocrinology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Hao Zhang
- Union Hospital, Department of Endocrinology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Ping Wang
- Precision Health Program, Department of Radiology, College of Human Medicine, Michigan State University, East Lansing, MI, USA
| | - Xiang Hu
- Union Hospital, Department of Endocrinology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - LuLu Chen
- Union Hospital, Department of Endocrinology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
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Reich C, Meder B. The Heart and Artificial Intelligence-How Can We Improve Medicine Without Causing Harm. Curr Heart Fail Rep 2023; 20:271-279. [PMID: 37291432 PMCID: PMC10250175 DOI: 10.1007/s11897-023-00606-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/08/2023] [Indexed: 06/10/2023]
Abstract
PURPOSE OF REVIEW The introduction of Artificial Intelligence into the healthcare system offers enormous opportunities for biomedical research, the improvement of patient care, and cost reduction in high-end medicine. Digital concepts and workflows are already playing an increasingly important role in cardiology. The fusion of computer science and medicine offers great transformative potential and enables enormous acceleration processes in cardiovascular medicine. RECENT FINDINGS As medical data becomes smart, it is also becoming more valuable and vulnerable to malicious actors. In addition, the gap between what is technically possible and what is allowed by privacy legislation is growing. Principles of the General Data Protection Regulation that have been in force since May 2018, such as transparency, purpose limitation, and data minimization, seem to hinder the development and use of Artificial Intelligence. Concepts to secure data integrity and incorporate legal and ethical principles can help to avoid the potential risks of digitization and may result in an European leadership in regard to privacy protection and AI. The following review provides an overview of relevant aspects of Artificial Intelligence and Machine Learning, highlights selected applications in cardiology, and discusses central ethical and legal considerations.
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Affiliation(s)
- Christoph Reich
- Department of Internal Medicine III, Precision Digital Health, University of Heidelberg, Im Neuenheimer Feld 410, 69120, Heidelberg, Germany
- Informatics for Life, Heidelberg, Germany
- German Center for Cardiovascular Research (DZHK), Heidelberg, Germany
| | - Benjamin Meder
- Department of Internal Medicine III, Precision Digital Health, University of Heidelberg, Im Neuenheimer Feld 410, 69120, Heidelberg, Germany.
- Informatics for Life, Heidelberg, Germany.
- German Center for Cardiovascular Research (DZHK), Heidelberg, Germany.
- Department of Genetics, Genome Technology Center, Stanford University, Stanford, CA, USA.
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