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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 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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Cheng A, Zhang Y, Qian Z, Yuan X, Yao S, Ni W, Zheng Y, Zhang H, Lu Q, Zhao Z. Integrating multi-task and cost-sensitive learning for predicting mortality risk of chronic diseases in the elderly using real-world data. Int J Med Inform 2024; 191:105567. [PMID: 39068894 DOI: 10.1016/j.ijmedinf.2024.105567] [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: 03/18/2024] [Revised: 07/22/2024] [Accepted: 07/23/2024] [Indexed: 07/30/2024]
Abstract
BACKGROUND AND OBJECTIVE Real-world data encompass population diversity, enabling insights into chronic disease mortality risk among the elderly. Deep learning excels on large datasets, offering promise for real-world data. However, current models focus on single diseases, neglecting comorbidities prevalent in patients. Moreover, mortality is infrequent compared to illness, causing extreme class imbalance that impedes reliable prediction. We aim to develop a deep learning framework that accurately forecasts mortality risk from real-world data by addressing comorbidities and class imbalance. METHODS We integrated multi-task and cost-sensitive learning, developing an enhanced deep neural network architecture that extends multi-task learning to predict mortality risk across multiple chronic diseases. Each patient cohort with a chronic disease was assigned to a separate task, with shared lower-level parameters capturing inter-disease complexities through distinct top-level networks. Cost-sensitive functions were incorporated to ensure learning of positive class characteristics for each task and achieve accurate prediction of the risk of death from multiple chronic diseases. RESULTS Our study covers 15 prevalent chronic diseases and is experimented with real-world data from 482,145 patients (including 9,516 deaths) in Shenzhen, China. The proposed model is compared with six models including three machine learning models: logistic regression, XGBoost, and CatBoost, and three state-of-the-art deep learning models: 1D-CNN, TabNet, and Saint. The experimental results show that, compared with the other compared algorithms, MTL-CSDNN has better prediction results on the test set (ACC=0.99, REC=0.99, PRAUC=0.97, MCC=0.98, G-means = 0.98). CONCLUSIONS Our method provides valuable insights into leveraging real-world data for precise multi-disease mortality risk prediction, offering potential applications in optimizing chronic disease management, enhancing well-being, and reducing healthcare costs for the elderly population.
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Affiliation(s)
- Aosheng Cheng
- Center for Studies of Information Resources, Wuhan University, Wuhan, China.
| | - Yan Zhang
- Department of Elderly Health Management, Shenzhen Center for Chronic Disease Control, Shenzhen, Guangdong, China.
| | - Zhiqiang Qian
- Center for Studies of Information Resources, Wuhan University, Wuhan, China; Big Data Research Institute, Wuhan University, Wuhan, China.
| | - Xueli Yuan
- Department of Elderly Health Management, Shenzhen Center for Chronic Disease Control, Shenzhen, Guangdong, China.
| | - Sumei Yao
- Center for Studies of Information Resources, Wuhan University, Wuhan, China; Big Data Research Institute, Wuhan University, Wuhan, China
| | - Wenqing Ni
- Department of Elderly Health Management, Shenzhen Center for Chronic Disease Control, Shenzhen, Guangdong, China.
| | - Yijin Zheng
- Department of Elderly Health Management, Shenzhen Center for Chronic Disease Control, Shenzhen, Guangdong, China.
| | - Hongmin Zhang
- Department of Elderly Health Management, Shenzhen Center for Chronic Disease Control, Shenzhen, Guangdong, China.
| | - Quan Lu
- Center for Studies of Information Resources, Wuhan University, Wuhan, China; Big Data Research Institute, Wuhan University, Wuhan, China.
| | - Zhiguang Zhao
- Department of Elderly Health Management, Shenzhen Center for Chronic Disease Control, Shenzhen, Guangdong, China.
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Petmezas G, Papageorgiou VE, Vassilikos V, Pagourelias E, Tsaklidis G, Katsaggelos AK, Maglaveras N. Recent advancements and applications of deep learning in heart failure: Α systematic review. Comput Biol Med 2024; 176:108557. [PMID: 38728995 DOI: 10.1016/j.compbiomed.2024.108557] [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: 03/15/2024] [Revised: 04/12/2024] [Accepted: 05/05/2024] [Indexed: 05/12/2024]
Abstract
BACKGROUND Heart failure (HF), a global health challenge, requires innovative diagnostic and management approaches. The rapid evolution of deep learning (DL) in healthcare necessitates a comprehensive review to evaluate these developments and their potential to enhance HF evaluation, aligning clinical practices with technological advancements. OBJECTIVE This review aims to systematically explore the contributions of DL technologies in the assessment of HF, focusing on their potential to improve diagnostic accuracy, personalize treatment strategies, and address the impact of comorbidities. METHODS A thorough literature search was conducted across four major electronic databases: PubMed, Scopus, Web of Science and IEEE Xplore, yielding 137 articles that were subsequently categorized into five primary application areas: cardiovascular disease (CVD) classification, HF detection, image analysis, risk assessment, and other clinical analyses. The selection criteria focused on studies utilizing DL algorithms for HF assessment, not limited to HF detection but extending to any attempt in analyzing and interpreting HF-related data. RESULTS The analysis revealed a notable emphasis on CVD classification and HF detection, with DL algorithms showing significant promise in distinguishing between affected individuals and healthy subjects. Furthermore, the review highlights DL's capacity to identify underlying cardiomyopathies and other comorbidities, underscoring its utility in refining diagnostic processes and tailoring treatment plans to individual patient needs. CONCLUSIONS This review establishes DL as a key innovation in HF management, highlighting its role in advancing diagnostic accuracy and personalized care. The insights provided advocate for the integration of DL in clinical settings and suggest directions for future research to enhance patient outcomes in HF care.
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Affiliation(s)
- Georgios Petmezas
- 2nd Department of Obstetrics and Gynecology, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece; Centre for Research and Technology Hellas, Thessaloniki, Greece.
| | | | - Vasileios Vassilikos
- 3rd Department of Cardiology, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Efstathios Pagourelias
- 3rd Department of Cardiology, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - George Tsaklidis
- Department of Mathematics, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Aggelos K Katsaggelos
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, USA
| | - Nicos Maglaveras
- 2nd Department of Obstetrics and Gynecology, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
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Balamurugan M, Meera DS. Hybrid optimized temporal convolutional networks with long short-term memory for heart disease prediction with deep features. Comput Methods Biomech Biomed Engin 2024:1-25. [PMID: 38584483 DOI: 10.1080/10255842.2024.2310075] [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: 09/16/2023] [Accepted: 01/10/2024] [Indexed: 04/09/2024]
Abstract
A heart attack is intended as top prevalent among all ruinous ailments. Day by day, the number of affected people count is increasing globally. The medical field is struggling to detect heart disease in the initial step. Early prediction can help patients to save their life. Thus, this paper implements a novel heart disease prediction model with the help of a hybrid deep learning strategy. The developed framework consists of various steps like (i) Data collection, (ii) Deep feature extraction, and (iii) Disease prediction. Initially, the standard medical data from various patients are acquired from the clinical standard datasets. Here, a One-Dimensional Convolutional Neural Network (1DCNN) is utilized for extracting the deep features from the acquired medical data to minimize the number of redundant data from the gathered large-scale data. The acquired deep features are directly fed to the Hybrid Optimized Deep Classifier (HODC) with the integration of Temporal Convolutional Networks (TCN) with Long Short-Term Memory (LSTM), where the parameters in both classifiers are optimized using the newly suggested Enhanced Forensic-Based Investigation (EFBI) inspired meta-optimization algorithm. Throughout the result analysis, the accuracy and precision rate of the offered approach is 98.67% and 99.48%. The evaluation outcomes show that the recommended system outperforms the extant systems in terms of performance metrics examination.
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Affiliation(s)
- M Balamurugan
- Research Scholar, Department of Computer Science and Engineering, Vels Institute of Science, Technology & Advanced Studies (VISTAS), Chennai, India
| | - Dr S Meera
- Associate Professor, Vels Institute of Science, Technology & Advanced Studies (VISTAS), Pallavaram, Chennai, Tamil Nadu, India
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Dadon Z, Rav Acha M, Orlev A, Carasso S, Glikson M, Gottlieb S, Alpert EA. Artificial Intelligence-Based Left Ventricular Ejection Fraction by Medical Students for Mortality and Readmission Prediction. Diagnostics (Basel) 2024; 14:767. [PMID: 38611680 PMCID: PMC11011323 DOI: 10.3390/diagnostics14070767] [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: 03/11/2024] [Revised: 03/28/2024] [Accepted: 03/31/2024] [Indexed: 04/14/2024] Open
Abstract
INTRODUCTION Point-of-care ultrasound has become a universal practice, employed by physicians across various disciplines, contributing to diagnostic processes and decision-making. AIM To assess the association of reduced (<50%) left-ventricular ejection fraction (LVEF) based on prospective point-of-care ultrasound operated by medical students using an artificial intelligence (AI) tool and 1-year primary composite outcome, including mortality and readmission for cardiovascular-related causes. METHODS Eight trained medical students used a hand-held ultrasound device (HUD) equipped with an AI-based tool for automatic evaluation of the LVEF of non-selected patients hospitalized in a cardiology department from March 2019 through March 2020. RESULTS The study included 82 patients (72 males aged 58.5 ± 16.8 years), of whom 34 (41.5%) were diagnosed with AI-based reduced LVEF. The rates of the composite outcome were higher among patients with reduced systolic function compared to those with preserved LVEF (41.2% vs. 16.7%, p = 0.014). Adjusting for pertinent variables, reduced LVEF independently predicted the composite outcome (HR 2.717, 95% CI 1.083-6.817, p = 0.033). As compared to those with LVEF ≥ 50%, patients with reduced LVEF had a longer length of stay and higher rates of the secondary composite outcome, including in-hospital death, advanced ventilatory support, shock, and acute decompensated heart failure. CONCLUSION AI-based assessment of reduced systolic function in the hands of medical students, independently predicted 1-year mortality and cardiovascular-related readmission and was associated with unfavorable in-hospital outcomes. AI utilization by novice users may be an important tool for risk stratification for hospitalized patients.
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Affiliation(s)
- Ziv Dadon
- Jesselson Integrated Heart Center, Eisenberg R&D Authority, Shaare Zedek Medical Center, Jerusalem 9103102, Israel
- Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112102, Israel
| | - Moshe Rav Acha
- Jesselson Integrated Heart Center, Eisenberg R&D Authority, Shaare Zedek Medical Center, Jerusalem 9103102, Israel
- Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112102, Israel
| | - Amir Orlev
- Jesselson Integrated Heart Center, Eisenberg R&D Authority, Shaare Zedek Medical Center, Jerusalem 9103102, Israel
- Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112102, Israel
| | - Shemy Carasso
- Jesselson Integrated Heart Center, Eisenberg R&D Authority, Shaare Zedek Medical Center, Jerusalem 9103102, Israel
- Azrieli Faculty of Medicine, Bar-Ilan University, Safed 1311502, Israel
| | - Michael Glikson
- Jesselson Integrated Heart Center, Eisenberg R&D Authority, Shaare Zedek Medical Center, Jerusalem 9103102, Israel
- Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112102, Israel
| | - Shmuel Gottlieb
- Jesselson Integrated Heart Center, Eisenberg R&D Authority, Shaare Zedek Medical Center, Jerusalem 9103102, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Evan Avraham Alpert
- Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112102, Israel
- Department of Emergency Medicine, Hadassah Medical Center—Ein Kerem, Jerusalem 9112001, Israel
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Rao S, Mamouei M, Salimi-Khorshidi G, Li Y, Ramakrishnan R, Hassaine A, Canoy D, Rahimi K. Targeted-BEHRT: Deep Learning for Observational Causal Inference on Longitudinal Electronic Health Records. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:5027-5038. [PMID: 35737602 DOI: 10.1109/tnnls.2022.3183864] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Observational causal inference is useful for decision-making in medicine when randomized clinical trials (RCTs) are infeasible or nongeneralizable. However, traditional approaches do not always deliver unconfounded causal conclusions in practice. The rise of "doubly robust" nonparametric tools coupled with the growth of deep learning for capturing rich representations of multimodal data offers a unique opportunity to develop and test such models for causal inference on comprehensive electronic health records (EHRs). In this article, we investigate causal modeling of an RCT-established causal association: the effect of classes of antihypertensive on incident cancer risk. We develop a transformer-based model, targeted bidirectional EHR transformer (T-BEHRT) coupled with doubly robust estimation to estimate average risk ratio (RR). We compare our model to benchmark statistical and deep learning models for causal inference in multiple experiments on semi-synthetic derivations of our dataset with various types and intensities of confounding. In order to further test the reliability of our approach, we test our model on situations of limited data. We find that our model provides more accurate estimates of relative risk [least sum absolute error (SAE) from ground truth] compared with benchmark estimations. Finally, our model provides an estimate of class-wise antihypertensive effect on cancer risk that is consistent with results derived from RCTs.
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Peng Y, Wang Y, Wen Z, Xiang H, Guo L, Su L, He Y, Pang H, Zhou P, Zhan X. Deep learning and machine learning predictive models for neurological function after interventional embolization of intracranial aneurysms. Front Neurol 2024; 15:1321923. [PMID: 38327618 PMCID: PMC10848172 DOI: 10.3389/fneur.2024.1321923] [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: 10/24/2023] [Accepted: 01/08/2024] [Indexed: 02/09/2024] Open
Abstract
Objective The objective of this study is to develop a model to predicts the postoperative Hunt-Hess grade in patients with intracranial aneurysms by integrating radiomics and deep learning technologies, using preoperative CTA imaging data. Thereby assisting clinical decision-making and improving the assessment and prognosis of postoperative neurological function. Methods This retrospective study encompassed 101 patients who underwent aneurysm embolization surgery. 851 radiomic features were extracted from CTA images. 512 deep learning features are extracted from last layer of ResNet50 deep convolutional neural network model. The feature screening process pipeline encompassed intraclass correlation coefficient analysis, principal component analysis, U test, spearman correlation analysis, minimum redundancy maximum relevance algorithm and Lasso regression, to identify features most correlated with postoperative Hunt-Hess grading. In the model construction phase, three distinct models were constructed: radiomics feature-based model (RSM), deep learning feature-based model (DLM), and deep learning-radiomics feature fusion model (DLRSCM). The study also calculated the radiomics score and combined it with clinical data to construct a Nomogram for predictive modeling. DLM, RSM and DLRSCM model was constructed by 9 base algorithms and 1 ensemble learning algorithm - Stacking ensemble model. Model performance was evaluated based on the area under the Receiver Operating Characteristic (ROC) curve (AUC), Matthews Correlation Coefficient (MCC), calibration curves, and decision curves analysis. Results 5 significant radiomic feature and 4 significant deep learning features were obtained through the feature selection process. These features were utilized for model construction. Bootstrap resampling method was used for internal validation of the models. In terms of model evaluation, the DLM model, the stacking ensemble algorithm results achieved an AUC of 0.959 and MCC of 0.815. In the RSM model, the stacking ensemble model AUC was 0.935 and MCC was 0.793. The stacking ensemble model in DLRSCM outperformed others, with an AUC of 0.968 and MCC of 0.820. Results indicated that the ANN performed optimally among all base models, while the stacked ensemble learning model exhibited the highest predictive performance. Conclusion This study demonstrates that the combination of radiomics and deep learning is an effective approach to predict the postoperative Hunt-Hess grade in patients with intracranial aneurysms. This holds significant value in the early identification of postoperative neurological complications and in enhancing clinical decision-making.
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Affiliation(s)
- Yan Peng
- Department of Interventional Medicine, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Yiren Wang
- School of Nursing, Southwest Medical University, Luzhou, China
- Wound Healing Basic Research and Clinical Application Key Laboratory of Luzhou, Southwest Medical University, Luzhou, China
| | - Zhongjian Wen
- School of Nursing, Southwest Medical University, Luzhou, China
- Wound Healing Basic Research and Clinical Application Key Laboratory of Luzhou, Southwest Medical University, Luzhou, China
| | - Hongli Xiang
- School of Nursing, Southwest Medical University, Luzhou, China
- Wound Healing Basic Research and Clinical Application Key Laboratory of Luzhou, Southwest Medical University, Luzhou, China
| | - Ling Guo
- Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Lei Su
- School of Medical Information and Engineering, Southwest Medical University, Luzhou, China
| | - Yongcheng He
- Department of Pharmacy, Sichuan Agriculture University, Chengdu, China
| | - Haowen Pang
- Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Ping Zhou
- Wound Healing Basic Research and Clinical Application Key Laboratory of Luzhou, Southwest Medical University, Luzhou, China
- Department of Nursing, The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Xiang Zhan
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
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Nakao YM, Nadarajah R, Shuweihdi F, Nakao K, Fuat A, Moore J, Bates C, Wu J, Gale C. Predicting incident heart failure from population-based nationwide electronic health records: protocol for a model development and validation study. BMJ Open 2024; 14:e073455. [PMID: 38253453 PMCID: PMC10806764 DOI: 10.1136/bmjopen-2023-073455] [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: 03/06/2023] [Accepted: 06/29/2023] [Indexed: 01/24/2024] Open
Abstract
INTRODUCTION Heart failure (HF) is increasingly common and associated with excess morbidity, mortality, and healthcare costs. Treatment of HF can alter the disease trajectory and reduce clinical events in HF. However, many cases of HF remain undetected until presentation with more advanced symptoms, often requiring hospitalisation. Predicting incident HF is challenging and statistical models are limited by performance and scalability in routine clinical practice. An HF prediction model implementable in nationwide electronic health records (EHRs) could enable targeted diagnostics to enable earlier identification of HF. METHODS AND ANALYSIS We will investigate a range of development techniques (including logistic regression and supervised machine learning methods) on routinely collected primary care EHRs to predict risk of new-onset HF over 1, 5 and 10 years prediction horizons. The Clinical Practice Research Datalink (CPRD)-GOLD dataset will be used for derivation (training and testing) and the CPRD-AURUM dataset for external validation. Both comprise large cohorts of patients, representative of the population of England in terms of age, sex and ethnicity. Primary care records are linked at patient level to secondary care and mortality data. The performance of the prediction model will be assessed by discrimination, calibration and clinical utility. We will only use variables routinely accessible in primary care. ETHICS AND DISSEMINATION Permissions for CPRD-GOLD and CPRD-AURUM datasets were obtained from CPRD (ref no: 21_000324). The CPRD ethical approval committee approved the study. The results will be submitted as a research paper for publication to a peer-reviewed journal and presented at peer-reviewed conferences. TRIAL REGISTRATION DETAILS The study was registered on Clinical Trials.gov (NCT05756127). A systematic review for the project was registered on PROSPERO (registration number: CRD42022380892).
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Affiliation(s)
- Yoko M Nakao
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK
- Leeds Institute for Data Analytics, University of Leeds, Leeds, UK
- Department of Pharmacoepidemiology, Graduate School of Medicine and Public Health, Kyoto University, Kyoto, Japan
| | - Ramesh Nadarajah
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK
- Leeds Institute for Data Analytics, University of Leeds, Leeds, UK
- Department of Cardiology, Leeds Teaching Hospital NHS Trust, Leeds, UK
| | - Farag Shuweihdi
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK
| | - Kazuhiro Nakao
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK
- Leeds Institute for Data Analytics, University of Leeds, Leeds, UK
- Department of Cardiovascular Medicine, National Cerebral and Cardiovascular Center, Suita, Japan
| | - Ahmet Fuat
- Carmel Medical Practice, Darlington & School of Medicine, Pharmacy and Health, Durham University, Darham, UK
| | - Jim Moore
- Stroke Road Surgery, Bishop's Cleeve, Cheltenham, UK
| | | | - Jianhua Wu
- Department of Biostatistics and Health Data Science, Queen Mary University of London, London, UK
| | - Chris Gale
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK
- Leeds Institute for Data Analytics, University of Leeds, Leeds, UK
- Department of Cardiology, Leeds Teaching Hospital NHS Trust, Leeds, UK
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Yoon M, Park JJ, Hur T, Hua CH, Hussain M, Lee S, Choi DJ. Application and Potential of Artificial Intelligence in Heart Failure: Past, Present, and Future. INTERNATIONAL JOURNAL OF HEART FAILURE 2024; 6:11-19. [PMID: 38303917 PMCID: PMC10827704 DOI: 10.36628/ijhf.2023.0050] [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: 09/05/2023] [Revised: 11/24/2023] [Accepted: 11/26/2023] [Indexed: 02/03/2024]
Abstract
The prevalence of heart failure (HF) is increasing, necessitating accurate diagnosis and tailored treatment. The accumulation of clinical information from patients with HF generates big data, which poses challenges for traditional analytical methods. To address this, big data approaches and artificial intelligence (AI) have been developed that can effectively predict future observations and outcomes, enabling precise diagnoses and personalized treatments of patients with HF. Machine learning (ML) is a subfield of AI that allows computers to analyze data, find patterns, and make predictions without explicit instructions. ML can be supervised, unsupervised, or semi-supervised. Deep learning is a branch of ML that uses artificial neural networks with multiple layers to find complex patterns. These AI technologies have shown significant potential in various aspects of HF research, including diagnosis, outcome prediction, classification of HF phenotypes, and optimization of treatment strategies. In addition, integrating multiple data sources, such as electrocardiography, electronic health records, and imaging data, can enhance the diagnostic accuracy of AI algorithms. Currently, wearable devices and remote monitoring aided by AI enable the earlier detection of HF and improved patient care. This review focuses on the rationale behind utilizing AI in HF and explores its various applications.
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Affiliation(s)
- Minjae Yoon
- Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
| | - Jin Joo Park
- Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
| | - Taeho Hur
- Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
- Department of Computer Science and Engineering, Kyung Hee University, Yongin, Korea
| | - Cam-Hao Hua
- Department of Computer Science and Engineering, Kyung Hee University, Yongin, Korea
| | - Musarrat Hussain
- Department of Computer Science and Engineering, Kyung Hee University, Yongin, Korea
| | - Sungyoung Lee
- Department of Computer Science and Engineering, Kyung Hee University, Yongin, Korea
| | - Dong-Ju Choi
- Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
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Rao S, Nazarzadeh M, Canoy D, Li Y, Huang J, Mamouei M, Salimi-Khorshidi G, Schutte AE, Neal B, Smith GD, Rahimi K. Sodium-based paracetamol: impact on blood pressure, cardiovascular events, and all-cause mortality. Eur Heart J 2023; 44:4448-4457. [PMID: 37611115 PMCID: PMC10635668 DOI: 10.1093/eurheartj/ehad535] [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: 03/19/2023] [Revised: 07/26/2023] [Accepted: 08/09/2023] [Indexed: 08/25/2023] Open
Abstract
BACKGROUND AND AIMS Effervescent formulations of paracetamol containing sodium bicarbonate have been reported to associate with increased blood pressure and a higher risk of cardiovascular diseases and all-cause mortality. Given the major implications of these findings, the reported associations were re-examined. METHODS Using linked electronic health records data, a cohort of 475 442 UK individuals with at least one prescription of paracetamol, aged between 60 and 90 years, was identified. Outcomes in patients taking sodium-based paracetamol were compared with those taking non-sodium-based formulations of the same. Using a deep learning approach, associations with systolic blood pressure (SBP), major cardiovascular events (myocardial infarction, heart failure, and stroke), and all-cause mortality within 1 year after baseline were investigated. RESULTS A total of 460 980 and 14 462 patients were identified for the non-sodium-based and sodium-based paracetamol exposure groups, respectively (mean age: 74 years; 64% women). Analysis revealed no difference in SBP [mean difference -0.04 mmHg (95% confidence interval -0.51, 0.43)] and no association with major cardiovascular events [relative risk (RR) 1.03 (0.91, 1.16)]. Sodium-based paracetamol showed a positive association with all-cause mortality [RR 1.46 (1.40, 1.52)]. However, after further accounting of other sources of residual confounding, the observed association attenuated towards the null [RR 1.08 (1.01, 1.16)]. Exploratory analyses revealed dysphagia and related conditions as major sources of uncontrolled confounding by indication for this association. CONCLUSIONS This study does not support previous suggestions of increased SBP and an elevated risk of cardiovascular events from short-term use of sodium bicarbonate paracetamol in routine clinical practice.
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Affiliation(s)
- Shishir Rao
- Deep Medicine, Oxford Martin School, University of Oxford, 34 Broad St, Oxford, OX1 3BD Oxfordshire, UK
- Nuffield Department of Women's & Reproductive Health, University of Oxford, Women's Centre (Level 3), John Radcliffe Hospital, Oxford, OX3 9DU Oxfordshire, UK
| | - Milad Nazarzadeh
- Deep Medicine, Oxford Martin School, University of Oxford, 34 Broad St, Oxford, OX1 3BD Oxfordshire, UK
- Nuffield Department of Women's & Reproductive Health, University of Oxford, Women's Centre (Level 3), John Radcliffe Hospital, Oxford, OX3 9DU Oxfordshire, UK
| | - Dexter Canoy
- Population Health Sciences Institute, Newcastle University, Newcastle, UK
| | - Yikuan Li
- Deep Medicine, Oxford Martin School, University of Oxford, 34 Broad St, Oxford, OX1 3BD Oxfordshire, UK
- Nuffield Department of Women's & Reproductive Health, University of Oxford, Women's Centre (Level 3), John Radcliffe Hospital, Oxford, OX3 9DU Oxfordshire, UK
| | - Jing Huang
- Deep Medicine, Oxford Martin School, University of Oxford, 34 Broad St, Oxford, OX1 3BD Oxfordshire, UK
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, China
| | - Mohammad Mamouei
- Deep Medicine, Oxford Martin School, University of Oxford, 34 Broad St, Oxford, OX1 3BD Oxfordshire, UK
- Nuffield Department of Women's & Reproductive Health, University of Oxford, Women's Centre (Level 3), John Radcliffe Hospital, Oxford, OX3 9DU Oxfordshire, UK
| | - Gholamreza Salimi-Khorshidi
- Deep Medicine, Oxford Martin School, University of Oxford, 34 Broad St, Oxford, OX1 3BD Oxfordshire, UK
- Nuffield Department of Women's & Reproductive Health, University of Oxford, Women's Centre (Level 3), John Radcliffe Hospital, Oxford, OX3 9DU Oxfordshire, UK
| | - Aletta E Schutte
- The George Institute for Global Health, University of New South Wales, Sydney, New South Wales, Australia
| | - Bruce Neal
- The George Institute for Global Health, University of New South Wales, Sydney, New South Wales, Australia
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK
- National Institute for Health Research Bristol Biomedical Research Centre, University Hospitals Bristol NHS Foundation Trust and University of Bristol, University of Bristol, Bristol, UK
| | - Kazem Rahimi
- Deep Medicine, Oxford Martin School, University of Oxford, 34 Broad St, Oxford, OX1 3BD Oxfordshire, UK
- Nuffield Department of Women's & Reproductive Health, University of Oxford, Women's Centre (Level 3), John Radcliffe Hospital, Oxford, OX3 9DU Oxfordshire, UK
- Population Health Sciences Institute, Newcastle University, Newcastle, UK
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11
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Nadarajah R, Younsi T, Romer E, Raveendra K, Nakao YM, Nakao K, Shuweidhi F, Hogg DC, Arbel R, Zahger D, Iakobishvili Z, Fonarow GC, Petrie MC, Wu J, Gale CP. Prediction models for heart failure in the community: A systematic review and meta-analysis. Eur J Heart Fail 2023; 25:1724-1738. [PMID: 37403669 DOI: 10.1002/ejhf.2970] [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: 05/15/2023] [Revised: 05/25/2023] [Accepted: 07/01/2023] [Indexed: 07/06/2023] Open
Abstract
AIMS Multivariable prediction models can be used to estimate risk of incident heart failure (HF) in the general population. A systematic review and meta-analysis was performed to determine the performance of models. METHODS AND RESULTS From inception to 3 November 2022 MEDLINE and EMBASE databases were searched for studies of multivariable models derived, validated and/or augmented for HF prediction in community-based cohorts. Discrimination measures for models with c-statistic data from ≥3 cohorts were pooled by Bayesian meta-analysis, with heterogeneity assessed through a 95% prediction interval (PI). Risk of bias was assessed using PROBAST. We included 36 studies with 59 prediction models. In meta-analysis, the Atherosclerosis Risk in Communities (ARIC) risk score (summary c-statistic 0.802, 95% confidence interval [CI] 0.707-0.883), GRaph-based Attention Model (GRAM; 0.791, 95% CI 0.677-0.885), Pooled Cohort equations to Prevent Heart Failure (PCP-HF) white men model (0.820, 95% CI 0.792-0.843), PCP-HF white women model (0.852, 95% CI 0.804-0.895), and REverse Time AttentIoN model (RETAIN; 0.839, 95% CI 0.748-0.916) had a statistically significant 95% PI and excellent discrimination performance. The ARIC risk score and PCP-HF models had significant summary discrimination among cohorts with a uniform prediction window. 77% of model results were at high risk of bias, certainty of evidence was low, and no model had a clinical impact study. CONCLUSIONS Prediction models for estimating risk of incident HF in the community demonstrate excellent discrimination performance. Their usefulness remains uncertain due to high risk of bias, low certainty of evidence, and absence of clinical effectiveness research.
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Affiliation(s)
- Ramesh Nadarajah
- Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK
- Leeds Institute of Data Analytics, University of Leeds, Leeds, UK
- Department of Cardiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Tanina Younsi
- Department of Cardiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Elizabeth Romer
- Department of Cardiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | | | - Yoko M Nakao
- Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK
- Leeds Institute of Data Analytics, University of Leeds, Leeds, UK
| | - Kazuhiro Nakao
- Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK
- Leeds Institute of Data Analytics, University of Leeds, Leeds, UK
- Department of Cardiovascular Medicine, National Cerebral and Cardiovascular Center, Suita, Japan
| | | | - David C Hogg
- School of Computing, University of Leeds, Leeds, UK
| | - Ronen Arbel
- Community Medical Services Division, Clalit Health Services, Tel Aviv, Israel
- Maximizing Health Outcomes Research Lab, Sapir College, Sderot, Israel
| | - Doron Zahger
- Department of Cardiology, Soroka University Medical Center, Beer Sheva, Israel
- Faculty of Health Sciences, Ben Gurion University of the Negev, Beer Sheva, Israel
| | - Zaza Iakobishvili
- Faculty of Health Sciences, Ben Gurion University of the Negev, Beer Sheva, Israel
- Department of Community Cardiology, Clalit Health Fund, Tel Aviv, Israel
| | - Gregg C Fonarow
- Division of Cardiology, Department of Medicine, University of California at Los Angeles, Los Angeles, CA, USA
| | - Mark C Petrie
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Jianhua Wu
- School of Dentistry, University of Leeds, Leeds, UK
- Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | - Chris P Gale
- Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK
- Leeds Institute of Data Analytics, University of Leeds, Leeds, UK
- Department of Cardiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
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12
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Mahmud I, Kabir MM, Mridha MF, Alfarhood S, Safran M, Che D. Cardiac Failure Forecasting Based on Clinical Data Using a Lightweight Machine Learning Metamodel. Diagnostics (Basel) 2023; 13:2540. [PMID: 37568902 PMCID: PMC10417090 DOI: 10.3390/diagnostics13152540] [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: 06/19/2023] [Revised: 07/24/2023] [Accepted: 07/28/2023] [Indexed: 08/13/2023] Open
Abstract
Accurate prediction of heart failure can help prevent life-threatening situations. Several factors contribute to the risk of heart failure, including underlying heart diseases such as coronary artery disease or heart attack, diabetes, hypertension, obesity, certain medications, and lifestyle habits such as smoking and excessive alcohol intake. Machine learning approaches to predict and detect heart disease hold significant potential for clinical utility but face several challenges in their development and implementation. This research proposes a machine learning metamodel for predicting a patient's heart failure based on clinical test data. The proposed metamodel was developed based on Random Forest Classifier, Gaussian Naive Bayes, Decision Tree models, and k-Nearest Neighbor as the final estimator. The metamodel is trained and tested utilizing a combined dataset comprising five well-known heart datasets (Statlog Heart, Cleveland, Hungarian, Switzerland, and Long Beach), all sharing 11 standard features. The study shows that the proposed metamodel can predict heart failure more accurately than other machine learning models, with an accuracy of 87%.
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Affiliation(s)
- Istiak Mahmud
- Department of Electrical and Electronic Engineering, Ahsanullah University of Science and Technology, Dhaka 1208, Bangladesh;
| | - Md Mohsin Kabir
- Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh;
| | - M. F. Mridha
- Department of Computer Science, American International University-Bangladesh, Dhaka 1229, Bangladesh
| | - Sultan Alfarhood
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia;
| | - Mejdl Safran
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia;
| | - Dunren Che
- School of Computing, Southern Illinois University, Carbondale, IL 62901, USA;
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13
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Rao S, Nazarzadeh M, Li Y, Canoy D, Mamouei M, Salimi-Khorshidi G, Rahimi K. Systolic blood pressure, chronic obstructive pulmonary disease and cardiovascular risk. Heart 2023; 109:1216-1222. [PMID: 37080767 PMCID: PMC10423512 DOI: 10.1136/heartjnl-2023-322431] [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: 01/18/2023] [Accepted: 04/04/2023] [Indexed: 04/22/2023] Open
Abstract
OBJECTIVE In individuals with complex underlying health problems, the association between systolic blood pressure (SBP) and cardiovascular disease is less well recognised. The association between SBP and risk of cardiovascular events in patients with chronic obstructive pulmonary disease (COPD) was investigated. METHODS AND ANALYSIS In this cohort study, 39 602 individuals with a diagnosis of COPD aged 55-90 years between 1990 and 2009 were identified from validated electronic health records (EHR) in the UK. The association between SBP and risk of cardiovascular end points (composite of ischaemic heart disease, heart failure, stroke and cardiovascular death) was analysed using a deep learning approach. RESULTS In the selected cohort (46.5% women, median age 69 years), 10 987 cardiovascular events were observed over a median follow-up period of 3.9 years. The association between SBP and risk of cardiovascular end points was found to be monotonic; the lowest SBP exposure group of <120 mm Hg presented nadir of risk. With respect to reference SBP (between 120 and 129 mm Hg), adjusted risk ratios for the primary outcome were 0.99 (95% CI 0.93 to 1.05) for SBP of <120 mm Hg, 1.02 (0.97 to 1.07) for SBP between 130 and 139 mm Hg, 1.07 (1.01 to 1.12) for SBP between 140 and 149 mm Hg, 1.11 (1.05 to 1.17) for SBP between 150 and 159 mm Hg and 1.16 (1.10 to 1.22) for SBP ≥160 mm Hg. CONCLUSION Using deep learning for modelling EHR, we identified a monotonic association between SBP and risk of cardiovascular events in patients with COPD.
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Affiliation(s)
- Shishir Rao
- Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UK
- Deep Medicine, Oxford Martin School, University of Oxford, Oxford, UK
| | - Milad Nazarzadeh
- Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UK
- Deep Medicine, Oxford Martin School, University of Oxford, Oxford, UK
| | - Yikuan Li
- Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UK
- Deep Medicine, Oxford Martin School, University of Oxford, Oxford, UK
| | - Dexter Canoy
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Mohammad Mamouei
- Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UK
- Deep Medicine, Oxford Martin School, University of Oxford, Oxford, UK
| | - Gholamreza Salimi-Khorshidi
- Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UK
- Deep Medicine, Oxford Martin School, University of Oxford, Oxford, UK
| | - Kazem Rahimi
- Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UK
- Deep Medicine, Oxford Martin School, University of Oxford, Oxford, UK
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
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14
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Sun H, Jiang Q, Huang Y, Mo J, Xie W, Dong H, Jia Y. Integrated smart analytics of nucleic acid amplification tests via paper microfluidics and deep learning in cloud computing. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2023]
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15
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Veerabaku MG, Nithiyanantham J, Urooj S, Md AQ, Sivaraman AK, Tee KF. Intelligent Bi-LSTM with Architecture Optimization for Heart Disease Prediction in WBAN through Optimal Channel Selection and Feature Selection. Biomedicines 2023; 11:biomedicines11041167. [PMID: 37189784 DOI: 10.3390/biomedicines11041167] [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: 01/02/2023] [Revised: 03/02/2023] [Accepted: 03/22/2023] [Indexed: 05/17/2023] Open
Abstract
Wireless Body Area Network (WBAN) is a trending technology of Wireless Sensor Networks (WSN) to enhance the healthcare system. This system is developed to monitor individuals by observing their physical signals to offer physical activity status as a wearable low-cost system that is considered an unremarkable solution for continuous monitoring of cardiovascular health. Various studies have discussed the uses of WBAN in Personal Health Monitoring systems (PHM) based on real-world health monitoring models. The major goal of WBAN is to offer early and fast analysis of the individuals but it is not able to attain its potential by utilizing conventional expert systems and data mining. Multiple kinds of research are performed in WBAN based on routing, security, energy efficiency, etc. This paper suggests a new heart disease prediction under WBAN. Initially, the standard patient data regarding heart diseases are gathered from benchmark datasets using WBAN. Then, the channel selections for data transmission are carried out through the Improved Dingo Optimizer (IDOX) algorithm using a multi-objective function. Through the selected channel, the data are transmitted for the deep feature extraction process using One Dimensional-Convolutional Neural Networks (ID-CNN) and Autoencoder. Then, the optimal feature selections are done through the IDOX algorithm for getting more suitable features. Finally, the IDOX-based heart disease prediction is done by Modified Bidirectional Long Short-Term Memory (M-BiLSTM), where the hyperparameters of BiLSTM are tuned using the IDOX algorithm. Thus, the empirical outcomes of the given offered method show that it accurately categorizes a patient's health status founded on abnormal vital signs that is useful for providing the proper medical care to the patients.
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Affiliation(s)
- Muthu Ganesh Veerabaku
- Department of Electronics and Communication Engineering, K.L.N. College of Engineering, Pottapalayam 630612, India
| | - Janakiraman Nithiyanantham
- Department of Electronics and Communication Engineering, K.L.N. College of Engineering, Pottapalayam 630612, India
| | - Shabana Urooj
- Department of Electrical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Abdul Quadir Md
- School of Computer Science and Engineering, Vellore Institute of Technology, Chennai 600127, India
| | - Arun Kumar Sivaraman
- Digital Engineering Services, Photon Inc., DLF Cyber City, Chennai 600089, India
| | - Kong Fah Tee
- Department of Civil and Environmental Engineering, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
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16
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Bender BF, Berry JA. Trends in Passive IoT Biomarker Monitoring and Machine Learning for Cardiovascular Disease Management in the U.S. Elderly Population. ADVANCES IN GERIATRIC MEDICINE AND RESEARCH 2023; 5:e230002. [PMID: 37274061 PMCID: PMC10237513 DOI: 10.20900/agmr20230002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
It is predicted that the growth in the U.S. elderly population alongside continued growth in chronic disease prevalence will further strain an already overburdened healthcare system and could compromise the delivery of equitable care. Current trends in technology are demonstrating successful application of artificial intelligence (AI) and machine learning (ML) to biomarkers of cardiovascular disease (CVD) using longitudinal data collected passively from internet-of-things (IoT) platforms deployed among the elderly population. These systems are growing in sophistication and deployed across evermore use-cases, presenting new opportunities and challenges for innovators and caregivers alike. IoT sensor development that incorporates greater levels of passivity will increase the likelihood of continued growth in device adoption among the geriatric population for longitudinal health data collection which will benefit a variety of CVD applications. This growth in IoT sensor development and longitudinal data acquisition is paralleled by the growth in ML approaches that continue to provide promising avenues for better geriatric care through higher personalization, more real-time feedback, and prognostic insights that may help prevent downstream complications and relieve strain on the healthcare system overall. However, findings that identify differences in longitudinal biomarker interpretations between elderly populations and relatively younger populations highlights the necessity that ML approaches that use data from newly developed passive IoT systems should collect more data on this target population and more clinical trials will help elucidate the extent of benefits and risks from these data driven approaches to remote care.
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Affiliation(s)
| | - Jasmine A. Berry
- Robotics Institute, University of Michigan, College of Engineering, Ann Arbor, MI 48109, USA
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17
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Rao S, Li Y, Nazarzadeh M, Canoy D, Mamouei M, Hassaine A, Salimi-Khorshidi G, Rahimi K. Systolic Blood Pressure and Cardiovascular Risk in Patients With Diabetes: A Prospective Cohort Study. Hypertension 2023; 80:598-607. [PMID: 36583386 PMCID: PMC9944753 DOI: 10.1161/hypertensionaha.122.20489] [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] [Indexed: 12/31/2022]
Abstract
BACKGROUND Whether the association between systolic blood pressure (SBP) and risk of cardiovascular disease is monotonic or whether there is a nadir of optimal blood pressure remains controversial. We investigated the association between SBP and cardiovascular events in patients with diabetes across the full spectrum of SBP. METHODS A cohort of 49 000 individuals with diabetes aged 50 to 90 years between 1990 and 2005 was identified from linked electronic health records in the United Kingdom. Associations between SBP and cardiovascular outcomes (ischemic heart disease, heart failure, stroke, and cardiovascular death) were analyzed using a deep learning approach. RESULTS Over a median follow-up of 7.3 years, 16 378 cardiovascular events were observed. The relationship between SBP and cardiovascular events followed a monotonic pattern, with the group with the lowest baseline SBP of <120 mm Hg exhibiting the lowest risk of cardiovascular events. In comparison to the reference group with the lowest SBP (<120 mm Hg), the adjusted risk ratio for cardiovascular disease was 1.03 (95% CI, 0.97-1.10) for SBP between 120 and 129 mm Hg, 1.05 (0.99-1.11) for SBP between 130 and 139 mm Hg, 1.08 (1.01-1.15) for SBP between 140 and 149 mm Hg, 1.12 (1.03-1.20) for SBP between 150 and 159 mm Hg, and 1.19 (1.09-1.28) for SBP ≥160 mm Hg. CONCLUSIONS Using deep learning modeling, we found a monotonic relationship between SBP and risk of cardiovascular outcomes in patients with diabetes, without evidence of a J-shaped relationship.
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Affiliation(s)
- Shishir Rao
- Deep Medicine, Oxford Martin School, University of Oxford, Oxford, United Kingdom (S.R., Y.L., M.N., M.M., G.S.-K., K.R.).,Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, United Kingdom (S.R., Y.L., M.N., M.M., G.S.-K., K.R.)
| | - Yikuan Li
- Deep Medicine, Oxford Martin School, University of Oxford, Oxford, United Kingdom (S.R., Y.L., M.N., M.M., G.S.-K., K.R.).,Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, United Kingdom (S.R., Y.L., M.N., M.M., G.S.-K., K.R.)
| | - Milad Nazarzadeh
- Deep Medicine, Oxford Martin School, University of Oxford, Oxford, United Kingdom (S.R., Y.L., M.N., M.M., G.S.-K., K.R.).,Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, United Kingdom (S.R., Y.L., M.N., M.M., G.S.-K., K.R.)
| | - Dexter Canoy
- Population Health Sciences Institute, University of Newcastle, Newcastle, United Kingdom (D.C.)
| | - Mohammad Mamouei
- Deep Medicine, Oxford Martin School, University of Oxford, Oxford, United Kingdom (S.R., Y.L., M.N., M.M., G.S.-K., K.R.).,Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, United Kingdom (S.R., Y.L., M.N., M.M., G.S.-K., K.R.)
| | - Abdelaali Hassaine
- Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, United Kingdom (A.H.)
| | - Gholamreza Salimi-Khorshidi
- Deep Medicine, Oxford Martin School, University of Oxford, Oxford, United Kingdom (S.R., Y.L., M.N., M.M., G.S.-K., K.R.).,Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, United Kingdom (S.R., Y.L., M.N., M.M., G.S.-K., K.R.)
| | - Kazem Rahimi
- Deep Medicine, Oxford Martin School, University of Oxford, Oxford, United Kingdom (S.R., Y.L., M.N., M.M., G.S.-K., K.R.).,Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, United Kingdom (S.R., Y.L., M.N., M.M., G.S.-K., K.R.).,NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom (K.R.)
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18
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Canoy D, Nazarzadeh M, Copland E, Bidel Z, Rao S, Li Y, Rahimi K. How Much Lowering of Blood Pressure Is Required to Prevent Cardiovascular Disease in Patients With and Without Previous Cardiovascular Disease? Curr Cardiol Rep 2022; 24:851-860. [PMID: 35524880 PMCID: PMC9288358 DOI: 10.1007/s11886-022-01706-4] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/19/2022] [Indexed: 11/12/2022]
Abstract
PURPOSE OF REVIEW To review the recent large-scale randomised evidence on pharmacologic reduction in blood pressure for the primary and secondary prevention of cardiovascular disease. RECENT FINDINGS Based on findings of the meta-analysis of individual participant-level data from 48 randomised clinical trials and involving 344,716 participants with mean age of 65 years, the relative reduction in the risk of developing major cardiovascular events was proportional to the magnitude of achieved reduction in blood pressure. For each 5-mmHg reduction in systolic blood pressure, the risk of developing cardiovascular events fell by 10% (hazard ratio [HR] (95% confidence interval [CI], 0.90 [0.88 to 0.92]). When participants were stratified by their history of cardiovascular disease, the HRs (95% CI) in those with and without previous cardiovascular disease were 0.89 (0.86 to 0.92) and 0.91 (0.89 to 0.94), respectively, with no significant heterogeneity in these effects (adjusted P for interaction = 1.0). When these patient groups were further stratified by their baseline systolic blood pressure in increments of 10 mmHg from < 120 to ≥ 170 mmHg, there was no significant heterogeneity in the relative risk reduction across these categories in people with or without previous cardiovascular disease (adjusted P for interaction were 1.00 and 0.28, respectively). Pharmacologic lowering of blood pressure was effective in preventing major cardiovascular disease events both in people with or without previous cardiovascular disease, which was not modified by their baseline blood pressure level. Treatment effects were shown to be proportional to the intensity of blood pressure reduction, but even modest blood pressure reduction, on average, can lead to meaningful gains in the prevention of incident or recurrent cardiovascular disease.
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Affiliation(s)
- Dexter Canoy
- Deep Medicine, Nuffield Department of Women’s and Reproductive Health, University of Oxford, Hayes House 1F, 75 George St, Oxford, OX1 2BQ UK
- National Institutes of Health Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Milad Nazarzadeh
- Deep Medicine, Nuffield Department of Women’s and Reproductive Health, University of Oxford, Hayes House 1F, 75 George St, Oxford, OX1 2BQ UK
| | - Emma Copland
- Deep Medicine, Nuffield Department of Women’s and Reproductive Health, University of Oxford, Hayes House 1F, 75 George St, Oxford, OX1 2BQ UK
- National Institutes of Health Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Zeinab Bidel
- Deep Medicine, Nuffield Department of Women’s and Reproductive Health, University of Oxford, Hayes House 1F, 75 George St, Oxford, OX1 2BQ UK
- National Institutes of Health Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Shihir Rao
- Deep Medicine, Nuffield Department of Women’s and Reproductive Health, University of Oxford, Hayes House 1F, 75 George St, Oxford, OX1 2BQ UK
| | - Yikuan Li
- Deep Medicine, Nuffield Department of Women’s and Reproductive Health, University of Oxford, Hayes House 1F, 75 George St, Oxford, OX1 2BQ UK
| | - Kazem Rahimi
- Deep Medicine, Nuffield Department of Women’s and Reproductive Health, University of Oxford, Hayes House 1F, 75 George St, Oxford, OX1 2BQ UK
- National Institutes of Health Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
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