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Kaye AD, Kweon J, Hashim A, Elwaraky MM, Shehata IM, Luther PM, Shekoohi S. Evolving Concepts of Pain Management in Elderly Patients. Curr Pain Headache Rep 2024:10.1007/s11916-024-01291-x. [PMID: 38967713 DOI: 10.1007/s11916-024-01291-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/25/2024] [Indexed: 07/06/2024]
Abstract
PURPOSE OF REVIEW The elderly population typically suffer from a variety of diseases that mostly reflect the degenerative changes linked with the aging process. These diseases may be exacerbated by acute pain or by an abrupt aggravation of previously stable chronic pain. RECENT FINDINGS Physical and psychological changes associated with aging may influence one's experience of pain and, as a result, the severity of pain. Pain treatment in the elderly can be complex and is often a budgetary burden on the nation's health care system. These difficulties arise, in part, because of unanticipated pharmacodynamics, changed pharmacokinetics, and polypharmacy interactions. Therefore, it is critical to integrate a multidisciplinary team to develop a management strategy that incorporates medical, psychological, and surgical methods to control persistent pain conditions. It is in this critical process that pain prediction models can be of great use. The purpose of pain prediction models for the elderly is the use of mathematical models to predict the occurrence and intensity of pain and pain-related conditions. These mathematical models employ a vast quantity of data to ascertain the many risk factors for the development of pain problems in the elderly, whether said risks are adjustable or not. These models will pave the way for more informed medical decision making that are based on the findings of thousands of patients who have previously experienced the same illness and related pain conditions. However, future additional research needs to be undertaken to build prediction models that are not constrained by substantial legal or methodological limitations.
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Affiliation(s)
- Alan D Kaye
- Department of Anesthesiology, Louisiana State University Health Sciences Center at Shreveport, Shreveport, LA, 71103, USA
- Department of Pharmacology, Toxicology, and Neurosciences, Louisiana State University Health Sciences Center at Shreveport, Shreveport, LA, 71103, USA
| | - Jaeyeon Kweon
- School of Medicine, Louisiana State University Health Sciences Center at New Orleans, New Orleans, LA, 70112, USA
| | - Ahmed Hashim
- School of Medicine, Ain Shams University, Cairo, Egypt
| | | | | | - Patrick M Luther
- School of Medicine, Louisiana State University Health Sciences Center at Shreveport, Shreveport, LA, 71103, USA
| | - Sahar Shekoohi
- Department of Anesthesiology, Louisiana State University Health Sciences Center at Shreveport, Shreveport, LA, 71103, USA.
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Singh M, Kumar A, Khanna NN, Laird JR, Nicolaides A, Faa G, Johri AM, Mantella LE, Fernandes JFE, Teji JS, Singh N, Fouda MM, Singh R, Sharma A, Kitas G, Rathore V, Singh IM, Tadepalli K, Al-Maini M, Isenovic ER, Chaturvedi S, Garg D, Paraskevas KI, Mikhailidis DP, Viswanathan V, Kalra MK, Ruzsa Z, Saba L, Laine AF, Bhatt DL, Suri JS. Artificial intelligence for cardiovascular disease risk assessment in personalised framework: a scoping review. EClinicalMedicine 2024; 73:102660. [PMID: 38846068 PMCID: PMC11154124 DOI: 10.1016/j.eclinm.2024.102660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 04/25/2024] [Accepted: 05/08/2024] [Indexed: 06/09/2024] Open
Abstract
Background The field of precision medicine endeavors to transform the healthcare industry by advancing individualised strategies for diagnosis, treatment modalities, and predictive assessments. This is achieved by utilizing extensive multidimensional biological datasets encompassing diverse components, such as an individual's genetic makeup, functional attributes, and environmental influences. Artificial intelligence (AI) systems, namely machine learning (ML) and deep learning (DL), have exhibited remarkable efficacy in predicting the potential occurrence of specific cancers and cardiovascular diseases (CVD). Methods We conducted a comprehensive scoping review guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. Our search strategy involved combining key terms related to CVD and AI using the Boolean operator AND. In August 2023, we conducted an extensive search across reputable scholarly databases including Google Scholar, PubMed, IEEE Xplore, ScienceDirect, Web of Science, and arXiv to gather relevant academic literature on personalised medicine for CVD. Subsequently, in January 2024, we extended our search to include internet search engines such as Google and various CVD websites. These searches were further updated in March 2024. Additionally, we reviewed the reference lists of the final selected research articles to identify any additional relevant literature. Findings A total of 2307 records were identified during the process of conducting the study, consisting of 564 entries from external sites like arXiv and 1743 records found through database searching. After 430 duplicate articles were eliminated, 1877 items that remained were screened for relevancy. In this stage, 1241 articles remained for additional review after 158 irrelevant articles and 478 articles with insufficient data were removed. 355 articles were eliminated for being inaccessible, 726 for being written in a language other than English, and 281 for not having undergone peer review. Consequently, 121 studies were deemed suitable for inclusion in the qualitative synthesis. At the intersection of CVD, AI, and precision medicine, we found important scientific findings in our scoping review. Intricate pattern extraction from large, complicated genetic datasets is a skill that AI algorithms excel at, allowing for accurate disease diagnosis and CVD risk prediction. Furthermore, these investigations have uncovered unique genetic biomarkers linked to CVD, providing insight into the workings of the disease and possible treatment avenues. The construction of more precise predictive models and personalised treatment plans based on the genetic profiles of individual patients has been made possible by the revolutionary advancement of CVD risk assessment through the integration of AI and genomics. Interpretation The systematic methodology employed ensured the thorough examination of available literature and the inclusion of relevant studies, contributing to the robustness and reliability of the study's findings. Our analysis stresses a crucial point in terms of the adaptability and versatility of AI solutions. AI algorithms designed in non-CVD domains such as in oncology, often include ideas and tactics that might be modified to address cardiovascular problems. Funding No funding received.
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Affiliation(s)
- Manasvi Singh
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, 95661, USA
- Bennett University, 201310, Greater Noida, India
| | - Ashish Kumar
- Bennett University, 201310, Greater Noida, India
| | - Narendra N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, 110001, India
| | - John R. Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA, 94574, USA
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre and University of Nicosia Medical School, Cyprus
| | - Gavino Faa
- Department of Pathology, University of Cagliari, Cagliari, Italy
| | - Amer M. Johri
- Department of Medicine, Division of Cardiology, Queen's University, Kingston, Canada
| | - Laura E. Mantella
- Department of Medicine, Division of Cardiology, University of Toronto, Toronto, Canada
| | | | - Jagjit S. Teji
- Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, 60611, USA
| | - Narpinder Singh
- Department of Food Science and Technology, Graphic Era Deemed to Be University, Dehradun, Uttarakhand, 248002, India
| | - Mostafa M. Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID, 83209, USA
| | - Rajesh Singh
- Department of Research and Innovation, Uttaranchal Institute of Technology, Uttaranchal University, Dehradun, 248007, India
| | - Aditya Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, 22901, VA, USA
| | - George Kitas
- Academic Affairs, Dudley Group NHS Foundation Trust, DY1, Dudley, UK
| | - Vijay Rathore
- Nephrology Department, Kaiser Permanente, Sacramento, CA, 95823, USA
| | - Inder M. Singh
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, 95661, USA
| | | | - Mustafa Al-Maini
- Allergy, Clinical Immunology and Rheumatology Institute, Toronto, ON, L4Z 4C4, Canada
| | - Esma R. Isenovic
- Department of Radiobiology and Molecular Genetics, National Institute of The Republic of Serbia, University of Belgrade, 110010, Serbia
| | - Seemant Chaturvedi
- Department of Neurology & Stroke Program, University of Maryland, Baltimore, MD, USA
| | | | | | - Dimitri P. Mikhailidis
- Department of Clinical Biochemistry, Royal Free Hospital Campus, University College London Medical School, University College London (UCL), London, UK
| | | | | | - Zoltan Ruzsa
- Invasive Cardiology Division, University of Szeged, Szeged, Hungary
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria, 40138, Cagliari, Italy
| | - Andrew F. Laine
- Departments of Biomedical and Radiology, Columbia University, New York, NY, USA
| | | | - Jasjit S. Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, 95661, USA
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID, 83209, USA
- Department of Computer Science, Graphic Era Deemed to Be University, Dehradun, Uttarakhand, 248002, India
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Nasir K, Gullapelli R, Nicolas JC, Bose B, Nwana N, Butt SA, Shahid I, Cainzos-Achirica M, Patel K, Bhimaraj A, Javed Z, Andrieni J, Al-Kindi S, Jones SL, Zoghbi WA. Houston Methodist cardiovascular learning health system (CVD-LHS) registry: Methods for development and implementation of an automated electronic medical record-based registry using an informatics framework approach. Am J Prev Cardiol 2024; 18:100678. [PMID: 38756692 PMCID: PMC11096937 DOI: 10.1016/j.ajpc.2024.100678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 04/23/2024] [Accepted: 04/27/2024] [Indexed: 05/18/2024] Open
Abstract
Objectives To investigate the potential value and feasibility of creating a listing system-wide registry of patients with at-risk and established Atherosclerotic Cardiovascular Disease (ASCVD) within a large healthcare system using automated data extraction methods to systematically identify burden, determinants, and the spectrum of at-risk patients to inform population health management. Additionally, the Houston Methodist Cardiovascular Disease Learning Health System (HM CVD-LHS) registry intends to create high-quality data-driven analytical insights to assess, track, and promote cardiovascular research and care. Methods We conducted a retrospective multi-center, cohort analysis of adult patients who were seen in the outpatient settings of a large healthcare system between June 2016 - December 2022 to create an EMR-based registry. A common framework was developed to automatically extract clinical data from the EMR and then integrate it with the social determinants of health information retrieved from external sources. Microsoft's SQL Server Management Studio was used for creating multiple Extract-Transform-Load scripts and stored procedures for collecting, cleaning, storing, monitoring, reviewing, auto-updating, validating, and reporting the data based on the registry goals. Results A real-time, programmatically deidentified, auto-updated EMR-based HM CVD-LHS registry was developed with ∼450 variables stored in multiple tables each containing information related to patient's demographics, encounters, diagnoses, vitals, labs, medication use, and comorbidities. Out of 1,171,768 adult individuals in the registry, 113,022 (9.6%) ASCVD patients were identified between June 2016 and December 2022 (mean age was 69.2 ± 12.2 years, with 55% Men and 15% Black individuals). Further, multi-level groupings of patients with laboratory test results and medication use have been analyzed for evaluating the outcomes of interest. Conclusions HM CVD-LHS registry database was developed successfully providing the listing registry of patients with established ASCVD and those at risk. This approach empowers knowledge inference and provides support for efforts to move away from manual patient chart abstraction by suggesting that a common registry framework with a concurrent design of data collection tools and reporting rapidly extracting useful structured clinical data from EMRs for creating patient or specialty population registries.
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Affiliation(s)
- Khurram Nasir
- Division of Cardiovascular Prevention and Wellness, Department of Cardiology, Houston Methodist DeBakey Heart & Vascular Center, Houston, TX, United States
- Center for Health Data Science & Analytics, Houston Methodist Research Institute, Houston TX, United States
| | - Rakesh Gullapelli
- Center for Health Data Science & Analytics, Houston Methodist Research Institute, Houston TX, United States
| | - Juan C Nicolas
- Center for Health Data Science & Analytics, Houston Methodist Research Institute, Houston TX, United States
| | - Budhaditya Bose
- Center for Health Data Science & Analytics, Houston Methodist Research Institute, Houston TX, United States
| | - Nwabunie Nwana
- Center for Health Data Science & Analytics, Houston Methodist Research Institute, Houston TX, United States
| | - Sara Ayaz Butt
- Center for Health Data Science & Analytics, Houston Methodist Research Institute, Houston TX, United States
| | - Izza Shahid
- Division of Cardiovascular Prevention and Wellness, Department of Cardiology, Houston Methodist DeBakey Heart & Vascular Center, Houston, TX, United States
| | | | - Kershaw Patel
- Division of Cardiovascular Prevention and Wellness, Department of Cardiology, Houston Methodist DeBakey Heart & Vascular Center, Houston, TX, United States
| | - Arvind Bhimaraj
- Division of Cardiovascular Prevention and Wellness, Department of Cardiology, Houston Methodist DeBakey Heart & Vascular Center, Houston, TX, United States
| | - Zulqarnain Javed
- Center for Health Data Science & Analytics, Houston Methodist Research Institute, Houston TX, United States
| | - Julia Andrieni
- Population Health and Primary Care, Houston Methodist Hospital, Houston, TX, United States
| | - Sadeer Al-Kindi
- Division of Cardiovascular Prevention and Wellness, Department of Cardiology, Houston Methodist DeBakey Heart & Vascular Center, Houston, TX, United States
- Center for Health Data Science & Analytics, Houston Methodist Research Institute, Houston TX, United States
| | - Stephen L Jones
- Center for Health Data Science & Analytics, Houston Methodist Research Institute, Houston TX, United States
| | - William A Zoghbi
- Division of Cardiovascular Prevention and Wellness, Department of Cardiology, Houston Methodist DeBakey Heart & Vascular Center, Houston, TX, United States
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Koo BS, Jang M, Oh JS, Shin K, Lee S, Joo KB, Kim N, Kim TH. Machine learning models with time-series clinical features to predict radiographic progression in patients with ankylosing spondylitis. JOURNAL OF RHEUMATIC DISEASES 2024; 31:97-107. [PMID: 38559800 PMCID: PMC10973352 DOI: 10.4078/jrd.2023.0056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 10/15/2023] [Accepted: 10/30/2023] [Indexed: 04/04/2024]
Abstract
Objective Ankylosing spondylitis (AS) is chronic inflammatory arthritis causing structural damage and radiographic progression to the spine due to repeated and continuous inflammation over a long period. This study establishes the application of machine learning models to predict radiographic progression in AS patients using time-series data from electronic medical records (EMRs). Methods EMR data, including baseline characteristics, laboratory findings, drug administration, and modified Stoke AS Spine Score (mSASSS), were collected from 1,123 AS patients between January 2001 and December 2018 at a single center at the time of first (T1), second (T2), and third (T3) visits. The radiographic progression of the (n+1)th visit (Pn+1=(mSASSSn+1-mSASSSn)/(Tn+1-Tn)≥1 unit per year) was predicted using follow-up visit datasets from T1 to Tn. We used three machine learning methods (logistic regression with the least absolute shrinkage and selection operation, random forest, and extreme gradient boosting algorithms) with three-fold cross-validation. Results The random forest model using the T1 EMR dataset best predicted the radiographic progression P2 among the machine learning models tested with a mean accuracy and area under the curves of 73.73% and 0.79, respectively. Among the T1 variables, the most important variables for predicting radiographic progression were in the order of total mSASSS, age, and alkaline phosphatase. Conclusion Prognosis predictive models using time-series data showed reasonable performance with clinical features of the first visit dataset when predicting radiographic progression.
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Affiliation(s)
- Bon San Koo
- Department of Internal Medicine, Inje University Ilsan Paik Hospital, Inje University College of Medicine, Seoul, Korea
| | - Miso Jang
- Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
- Department of Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Ji Seon Oh
- Department of Information Medicine, Big Data Research Center, Asan Medical Center, Seoul, Korea
| | - Keewon Shin
- Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Seunghun Lee
- Department of Radiology, Hanyang University Hospital for Rheumatic Diseases, Seoul, Korea
| | - Kyung Bin Joo
- Department of Radiology, Hanyang University Hospital for Rheumatic Diseases, Seoul, Korea
| | - Namkug Kim
- Department of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
- Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Tae-Hwan Kim
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul, Korea
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Su L, Liu S, Long Y, Chen C, Chen K, Chen M, Chen Y, Cheng Y, Cui Y, Ding Q, Ding R, Duan M, Gao T, Gu X, He H, He J, Hu B, Hu C, Huang R, Huang X, Jiang H, Jiang J, Lan Y, Li J, Li L, Li L, Li W, Li Y, Lin J, Luo X, Lyu F, Mao Z, Miao H, Shang X, Shang X, Shang Y, Shen Y, Shi Y, Sun Q, Sun W, Tang Z, Wang B, Wang H, Wang H, Wang L, Wang L, Wang S, Wang Z, Wang Z, Wei D, Wu J, Wu Q, Xing X, Yang J, Yang X, Yu J, Yu W, Yu Y, Yuan H, Zhai Q, Zhang H, Zhang L, Zhang M, Zhang Z, Zhao C, Zheng R, Zhong L, Zhou F, Zhu W. Chinese experts' consensus on the application of intensive care big data. Front Med (Lausanne) 2024; 10:1174429. [PMID: 38264049 PMCID: PMC10804886 DOI: 10.3389/fmed.2023.1174429] [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: 02/26/2023] [Accepted: 11/09/2023] [Indexed: 01/25/2024] Open
Abstract
The development of intensive care medicine is inseparable from the diversified monitoring data. Intensive care medicine has been closely integrated with data since its birth. Critical care research requires an integrative approach that embraces the complexity of critical illness and the computational technology and algorithms that can make it possible. Considering the need of standardization of application of big data in intensive care, Intensive Care Medicine Branch of China Health Information and Health Care Big Data Society, Standard Committee has convened expert group, secretary group and the external audit expert group to formulate Chinese Experts' Consensus on the Application of Intensive Care Big Data (2022). This consensus makes 29 recommendations on the following five parts: Concept of intensive care big data, Important scientific issues, Standards and principles of database, Methodology in solving big data problems, Clinical application and safety consideration of intensive care big data. The consensus group believes this consensus is the starting step of application big data in the field of intensive care. More explorations and big data based retrospective research should be carried out in order to enhance safety and reliability of big data based models of critical care field.
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Affiliation(s)
- Longxiang Su
- Department of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Shengjun Liu
- Department of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Yun Long
- Department of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Chaodong Chen
- Department of Surgical Intensive Critical Unit, Beijing Chao-yang Hospital, Capital Medical University, Beijing, China
| | - Kai Chen
- Department of Critical Care Medicine, Fujian Provincial Key Laboratory of Critical Care Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fujian Provincial Center for Critical Care Medicine, Fuzhou, Fujian, China
| | - Ming Chen
- Department of Critical Care Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu, China
| | - Yaolong Chen
- Evidence-based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
| | - Yisong Cheng
- Department of Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Yating Cui
- Department of Critical Care Medicine, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Qi Ding
- Department of Surgical Intensive Critical Unit, Beijing Chao-yang Hospital, Capital Medical University, Beijing, China
| | - Renyu Ding
- Department of Intensive Care Unit, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Meili Duan
- Department of Critical Care Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Tao Gao
- Department of Critical Care Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu, China
| | - Xiaohua Gu
- Department of Critical Care Medicine, Northern Jiangsu People’s Hospital; Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Hongli He
- Intensive Care Unit, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, School of Medicine of University of Electronic Science and Technology, Chengdu, China
| | - Jiawei He
- Department of Critical Care Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Bo Hu
- Department of Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Chang Hu
- Department of Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Rui Huang
- Department of Critical Care Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Xiaobo Huang
- Intensive Care Unit, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, School of Medicine of University of Electronic Science and Technology, Chengdu, China
| | - Huizhen Jiang
- Department of Information Center, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Jing Jiang
- Department of Critical Care Medicine, Chongqing General Hospital, Chongqing, China
| | - Yunping Lan
- Intensive Care Unit, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, School of Medicine of University of Electronic Science and Technology, Chengdu, China
| | - Jun Li
- Department of Critical Care Medicine, Fujian Provincial Key Laboratory of Critical Care Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fujian Provincial Center for Critical Care Medicine, Fuzhou, Fujian, China
| | - Linfeng Li
- Medical Data Research Institute, Chongqing Medical University, Chongqing, China
| | - Lu Li
- Department of Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Wenxiong Li
- Department of Surgical Intensive Critical Unit, Beijing Chao-yang Hospital, Capital Medical University, Beijing, China
| | - Yongzai Li
- Information Network Center, QiLu Hospital, ShanDong University, Jinan, China
| | - Jin Lin
- Department of Critical Care Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Xufei Luo
- Evidence-based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
| | - Feng Lyu
- Department of Computer Science and Engineering, Central South University, Changsha, China
| | - Zhi Mao
- Department of Critical Care Medicine, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - He Miao
- Department of Intensive Care Unit, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Xiaopu Shang
- Department of Information Management, Beijing Jiaotong University, Beijing, China
| | - Xiuling Shang
- Department of Critical Care Medicine, Fujian Provincial Key Laboratory of Critical Care Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fujian Provincial Center for Critical Care Medicine, Fuzhou, Fujian, China
| | - You Shang
- Department of Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yuwen Shen
- Intensive Care Unit of Cardiovascular Surgery Department, Qilu Hospital of Shandong University, Jinan, China
| | - Yinghuan Shi
- National Institute of Healthcare Data Science, Nanjing University, Nanjing, China
| | - Qihang Sun
- British Chinese Society of Health Informatics, Beijing, China
| | - Weijun Sun
- Faculty of Automation, Guangdong University of Technology, Guangzhou, China
| | - Zhiyun Tang
- Department of Intensive Care Unit, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Emergency and Intensive Care Unit Center, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Bo Wang
- Department of Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Haijun Wang
- Department of Intensive Care Unit, National Cancer Center/National Clinical Research Center, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Hongliang Wang
- Department of Critical Care Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Li Wang
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences; School of Basic Medicine Peking Union Medical College, Beijing, China
| | - Luhao Wang
- Department of Critical Care Medicine, Sun Yat-Sen University First Affiliated Hospital, Guangzhou, China
| | - Sicong Wang
- Department of Critical Care Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Zhanwen Wang
- Intensive Care Unit, XiangYa Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiang Ya Hospital, Central South University, Changsha, China
- Hunan Provincial Clinical Research Center for Critical Care Medicine, Xiang Ya Hospital, Central South University, Changsha, China
| | - Zhong Wang
- Department of Intensive Care Unit, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Dong Wei
- National Institute of Healthcare Data Science, Nanjing University, Nanjing, China
| | - Jianfeng Wu
- Intensive Care Unit, XiangYa Hospital, Central South University, Changsha, China
| | - Qin Wu
- Department of Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Xuezhong Xing
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences; School of Basic Medicine Peking Union Medical College, Beijing, China
| | - Jin Yang
- Department of Critical Care Medicine, Chongqing General Hospital, Chongqing, China
| | - Xianghong Yang
- Department of Intensive Care Unit, National Cancer Center/National Clinical Research Center, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jiangquan Yu
- Department of Critical Care Medicine, Northern Jiangsu People’s Hospital; Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Wenkui Yu
- Department of Critical Care Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu, China
| | - Yuan Yu
- Intensive Care Unit of Cardiovascular Surgery Department, Qilu Hospital of Shandong University, Jinan, China
| | - Hao Yuan
- Department of Critical Care Medicine, Sun Yat-Sen University First Affiliated Hospital, Guangzhou, China
| | - Qian Zhai
- National Institute of Healthcare Data Science, Nanjing University, Nanjing, China
| | - Hao Zhang
- Department of Intensive Care Unit, National Cancer Center/National Clinical Research Center, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lina Zhang
- Intensive Care Unit, XiangYa Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiang Ya Hospital, Central South University, Changsha, China
- Hunan Provincial Clinical Research Center for Critical Care Medicine, Xiang Ya Hospital, Central South University, Changsha, China
| | - Meng Zhang
- Department of Critical Care Medicine, Chongqing General Hospital, Chongqing, China
| | - Zhongheng Zhang
- Department of Emergency Medicine, Key Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chunguang Zhao
- Intensive Care Unit, XiangYa Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiang Ya Hospital, Central South University, Changsha, China
- Hunan Provincial Clinical Research Center for Critical Care Medicine, Xiang Ya Hospital, Central South University, Changsha, China
| | - Ruiqiang Zheng
- Department of Critical Care Medicine, Northern Jiangsu People’s Hospital; Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Lei Zhong
- Department of Intensive Care Unit, National Cancer Center/National Clinical Research Center, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Feihu Zhou
- Department of Critical Care Medicine, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Weiguo Zhu
- Department of General Medicine, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
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Segar MW, Keshvani N, Pandey A. From prediction to prevention: The role of heart failure risk models: Heart to Heart: The Promise and Pitfalls of Heart Failure Risk Prediction Models. Eur J Heart Fail 2023; 25:1739-1741. [PMID: 37702311 DOI: 10.1002/ejhf.3034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 09/05/2023] [Accepted: 09/06/2023] [Indexed: 09/14/2023] Open
Affiliation(s)
- Matthew W Segar
- Department of Cardiology, Texas Heart Institute, Houston, TX, USA
| | - Neil Keshvani
- Division of Cardiology, Department of Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Ambarish Pandey
- Division of Cardiology, Department of Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA
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7
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Sedlakova J, Daniore P, Horn Wintsch A, Wolf M, Stanikic M, Haag C, Sieber C, Schneider G, Staub K, Alois Ettlin D, Grübner O, Rinaldi F, von Wyl V. Challenges and best practices for digital unstructured data enrichment in health research: A systematic narrative review. PLOS DIGITAL HEALTH 2023; 2:e0000347. [PMID: 37819910 PMCID: PMC10566734 DOI: 10.1371/journal.pdig.0000347] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 08/14/2023] [Indexed: 10/13/2023]
Abstract
Digital data play an increasingly important role in advancing health research and care. However, most digital data in healthcare are in an unstructured and often not readily accessible format for research. Unstructured data are often found in a format that lacks standardization and needs significant preprocessing and feature extraction efforts. This poses challenges when combining such data with other data sources to enhance the existing knowledge base, which we refer to as digital unstructured data enrichment. Overcoming these methodological challenges requires significant resources and may limit the ability to fully leverage their potential for advancing health research and, ultimately, prevention, and patient care delivery. While prevalent challenges associated with unstructured data use in health research are widely reported across literature, a comprehensive interdisciplinary summary of such challenges and possible solutions to facilitate their use in combination with structured data sources is missing. In this study, we report findings from a systematic narrative review on the seven most prevalent challenge areas connected with the digital unstructured data enrichment in the fields of cardiology, neurology and mental health, along with possible solutions to address these challenges. Based on these findings, we developed a checklist that follows the standard data flow in health research studies. This checklist aims to provide initial systematic guidance to inform early planning and feasibility assessments for health research studies aiming combining unstructured data with existing data sources. Overall, the generality of reported unstructured data enrichment methods in the studies included in this review call for more systematic reporting of such methods to achieve greater reproducibility in future studies.
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Affiliation(s)
- Jana Sedlakova
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland
- Institute of Biomedical Ethics and History of Medicine, University of Zurich, Zurich, Switzerland
| | - Paola Daniore
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland
| | - Andrea Horn Wintsch
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Center for Gerontology, University of Zurich, Zurich, Switzerland
- CoupleSense: Health and Interpersonal Emotion Regulation Group, University Research Priority Program (URPP) Dynamics of Healthy Aging, University of Zurich, Zurich, Switzerland
| | - Markus Wolf
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Department of Psychology, University of Zurich, Zurich, Switzerland
| | - Mina Stanikic
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Christina Haag
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Chloé Sieber
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Gerold Schneider
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Department of Computational Linguistics, University of Zurich, Zurich, Switzerland
| | - Kaspar Staub
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Institute of Evolutionary Medicine, University of Zurich, Zurich, Switzerland
| | - Dominik Alois Ettlin
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Center of Dental Medicine, University of Zurich, Zurich, Switzerland
| | - Oliver Grübner
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Department of Geography, University of Zurich, Zurich, Switzerland
| | - Fabio Rinaldi
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Dalle Molle Institute for Artificial Intelligence (IDSIA), Switzerland
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Fondazione Bruno Kessler, Trento, Italy
- Swiss Institute of Bioinformatics, Switzerland
| | - Viktor von Wyl
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
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8
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Paik KE, Hicklen R, Kaggwa F, Puyat CV, Nakayama LF, Ong BA, Shropshire JNI, Villanueva C. Digital Determinants of Health: Health data poverty amplifies existing health disparities-A scoping review. PLOS DIGITAL HEALTH 2023; 2:e0000313. [PMID: 37824445 PMCID: PMC10569513 DOI: 10.1371/journal.pdig.0000313] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Accepted: 07/02/2023] [Indexed: 10/14/2023]
Abstract
Artificial intelligence (AI) and machine learning (ML) have an immense potential to transform healthcare as already demonstrated in various medical specialties. This scoping review focuses on the factors that influence health data poverty, by conducting a literature review, analysis, and appraisal of results. Health data poverty is often an unseen factor which leads to perpetuating or exacerbating health disparities. Improvements or failures in addressing health data poverty will directly impact the effectiveness of AI/ML systems. The potential causes are complex and may enter anywhere along the development process. The initial results highlighted studies with common themes of health disparities (72%), AL/ML bias (28%) and biases in input data (18%). To properly evaluate disparities that exist we recommend a strengthened effort to generate unbiased equitable data, improved understanding of the limitations of AI/ML tools, and rigorous regulation with continuous monitoring of the clinical outcomes of deployed tools.
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Affiliation(s)
- Kenneth Eugene Paik
- MIT Critical Data, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Rachel Hicklen
- Research Medical Library, MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Fred Kaggwa
- Department of Computer Science, Mbarara University of Science & Technology, Mbarara, Uganda
| | | | - Luis Filipe Nakayama
- MIT Critical Data, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Department of Ophthalmology, São Paulo Federal University, São Paulo, Brazil
| | - Bradley Ashley Ong
- Department of Neurology, Neurological Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
| | | | - Cleva Villanueva
- Instituto Politécnico Nacional, Escuela Superior de Medicina, Mexico City, Mexico
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9
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Evans EA, Geissler KH. Use of Big Data and Ethical Issues for Populations With Substance Use Disorder. VALUE IN HEALTH : THE JOURNAL OF THE INTERNATIONAL SOCIETY FOR PHARMACOECONOMICS AND OUTCOMES RESEARCH 2023; 26:1321-1324. [PMID: 36921899 PMCID: PMC10497717 DOI: 10.1016/j.jval.2023.02.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 02/15/2023] [Accepted: 02/28/2023] [Indexed: 06/18/2023]
Abstract
With expanding data availability and computing power, health research is increasingly relying on big data from a variety of sources. We describe a state-level effort to address aspects of the opioid epidemic through public health research, which has resulted in an expansive data resource combining dozens of administrative data sources in Massachusetts. The Massachusetts Public Health Data Warehouse is a public health innovation that serves as an example of how to address the complexities of balancing data privacy and access to data for public health and health services research. We discuss issues of data protection and data access, and provide recommendations for ethical data governance. Keeping these issues in mind, the use of this data resource has the potential to allow for transformative research on critical public health issues.
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Affiliation(s)
- Elizabeth A Evans
- Department of Health Promotion and Policy, School of Public Health and Health Sciences, University of Massachusetts Amherst, Amherst, MA, USA
| | - Kimberley H Geissler
- Department of Health Promotion and Policy, School of Public Health and Health Sciences, University of Massachusetts Amherst, Amherst, MA, USA.
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10
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Schulte T, Wurz T, Groene O, Bohnet-Joschko S. Big Data Analytics to Reduce Preventable Hospitalizations-Using Real-World Data to Predict Ambulatory Care-Sensitive Conditions. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:4693. [PMID: 36981600 PMCID: PMC10049041 DOI: 10.3390/ijerph20064693] [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: 01/26/2023] [Revised: 03/01/2023] [Accepted: 03/04/2023] [Indexed: 06/18/2023]
Abstract
The purpose of this study was to develop a prediction model to identify individuals and populations with a high risk of being hospitalized due to an ambulatory care-sensitive condition who might benefit from preventative actions or tailored treatment options to avoid subsequent hospital admission. A rate of 4.8% of all individuals observed had an ambulatory care-sensitive hospitalization in 2019 and 6389.3 hospital cases per 100,000 individuals could be observed. Based on real-world claims data, the predictive performance was compared between a machine learning model (Random Forest) and a statistical logistic regression model. One result was that both models achieve a generally comparable performance with c-values above 0.75, whereas the Random Forest model reached slightly higher c-values. The prediction models developed in this study reached c-values comparable to existing study results of prediction models for (avoidable) hospitalization from the literature. The prediction models were designed in such a way that they can support integrated care or public and population health interventions with little effort with an additional risk assessment tool in the case of availability of claims data. For the regions analyzed, the logistic regression revealed that switching to a higher age class or to a higher level of long-term care and unit from prior hospitalizations (all-cause and due to an ambulatory care-sensitive condition) increases the odds of having an ambulatory care-sensitive hospitalization in the upcoming year. This is also true for patients with prior diagnoses from the diagnosis groups of maternal disorders related to pregnancy, mental disorders due to alcohol/opioids, alcoholic liver disease and certain diseases of the circulatory system. Further model refinement activities and the integration of additional data, such as behavioral, social or environmental data would improve both model performance and the individual risk scores. The implementation of risk scores identifying populations potentially benefitting from public health and population health activities would be the next step to enable an evaluation of whether ambulatory care-sensitive hospitalizations can be prevented.
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Affiliation(s)
- Timo Schulte
- Faculty of Management, Economics and Society, Witten/Herdecke University, 58455 Witten, Germany
- Faculty of Health, Witten/Herdecke University, 58455 Witten, Germany
- Department of Business Analytics, Clinics of Maerkischer Kreis, 58515 Luedenscheid, Germany
| | - Tillmann Wurz
- Department of Project and Change Management, University Clinic Hamburg-Eppendorf, 20251 Hamburg, Germany
| | - Oliver Groene
- Faculty of Management, Economics and Society, Witten/Herdecke University, 58455 Witten, Germany
- Department of Research & Innovation, OptiMedis AG, 20095 Hamburg, Germany
| | - Sabine Bohnet-Joschko
- Faculty of Management, Economics and Society, Witten/Herdecke University, 58455 Witten, Germany
- Faculty of Health, Witten/Herdecke University, 58455 Witten, Germany
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11
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Tonegawa-Kuji R, Kanaoka K, Iwanaga Y. Current status of real-world big data research in the cardiovascular field in Japan. J Cardiol 2023; 81:307-315. [PMID: 36126909 DOI: 10.1016/j.jjcc.2022.09.006] [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] [Received: 08/30/2022] [Accepted: 09/02/2022] [Indexed: 02/01/2023]
Abstract
Real-world data (RWD) are observational data obtained by collecting, structuring, and accumulating patient information among the medical big data. RWD are derived from a variety of patient medical care and health information outside of conventional research data, and include electronic health records, claims data, registry data of disease, drug and device, health check-up data, and more recently, patient information data from wearable devices. They are currently being utilized in various forms for optimal medical care and real-world evidence (RWE) is constructed through a process of hypothesis generation and verification based on the RWD research. Together with classic clinical research and pragmatic trials, RWE shapes the learning healthcare system and contributes to the improvement of medical care. In the cardiovascular medical care of the current super-aged society, the need for a variety of RWE and the research is increasing, since the guidelines established over time and the medical care based on it cannot necessarily be the best in accordance with the current medical situation. In this review, we focus on the RWD and RWE studies in the cardiovascular medical field and outlines their current status in Japan. Furthermore, we discuss the potential for extending the studies and issues related to the use of medical big data and RWD.
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Affiliation(s)
- Reina Tonegawa-Kuji
- Department of Medical and Health Information Management, National Cerebral and Cardiovascular Center, Suita, Japan
| | - Koshiro Kanaoka
- Department of Medical and Health Information Management, National Cerebral and Cardiovascular Center, Suita, Japan
| | - Yoshitaka Iwanaga
- Department of Medical and Health Information Management, National Cerebral and Cardiovascular Center, Suita, Japan.
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12
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Gill SK, Karwath A, Uh HW, Cardoso VR, Gu Z, Barsky A, Slater L, Acharjee A, Duan J, Dall'Olio L, el Bouhaddani S, Chernbumroong S, Stanbury M, Haynes S, Asselbergs FW, Grobbee DE, Eijkemans MJC, Gkoutos GV, Kotecha D. Artificial intelligence to enhance clinical value across the spectrum of cardiovascular healthcare. Eur Heart J 2023; 44:713-725. [PMID: 36629285 PMCID: PMC9976986 DOI: 10.1093/eurheartj/ehac758] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 11/22/2022] [Accepted: 12/05/2022] [Indexed: 01/12/2023] Open
Abstract
Artificial intelligence (AI) is increasingly being utilized in healthcare. This article provides clinicians and researchers with a step-wise foundation for high-value AI that can be applied to a variety of different data modalities. The aim is to improve the transparency and application of AI methods, with the potential to benefit patients in routine cardiovascular care. Following a clear research hypothesis, an AI-based workflow begins with data selection and pre-processing prior to analysis, with the type of data (structured, semi-structured, or unstructured) determining what type of pre-processing steps and machine-learning algorithms are required. Algorithmic and data validation should be performed to ensure the robustness of the chosen methodology, followed by an objective evaluation of performance. Seven case studies are provided to highlight the wide variety of data modalities and clinical questions that can benefit from modern AI techniques, with a focus on applying them to cardiovascular disease management. Despite the growing use of AI, further education for healthcare workers, researchers, and the public are needed to aid understanding of how AI works and to close the existing gap in knowledge. In addition, issues regarding data access, sharing, and security must be addressed to ensure full engagement by patients and the public. The application of AI within healthcare provides an opportunity for clinicians to deliver a more personalized approach to medical care by accounting for confounders, interactions, and the rising prevalence of multi-morbidity.
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Affiliation(s)
- Simrat K Gill
- Institute of Cardiovascular Sciences, University of Birmingham, Vincent Drive, B15 2TT Birmingham, UK
- Health Data Research UK Midlands, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Andreas Karwath
- Health Data Research UK Midlands, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Institute of Cancer and Genomic Sciences, University of Birmingham, Vincent Drive, B15 2TT Birmingham, UK
| | - Hae-Won Uh
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Victor Roth Cardoso
- Institute of Cardiovascular Sciences, University of Birmingham, Vincent Drive, B15 2TT Birmingham, UK
- Health Data Research UK Midlands, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Institute of Cancer and Genomic Sciences, University of Birmingham, Vincent Drive, B15 2TT Birmingham, UK
| | - Zhujie Gu
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Andrey Barsky
- Health Data Research UK Midlands, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Institute of Cancer and Genomic Sciences, University of Birmingham, Vincent Drive, B15 2TT Birmingham, UK
| | - Luke Slater
- Health Data Research UK Midlands, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Institute of Cancer and Genomic Sciences, University of Birmingham, Vincent Drive, B15 2TT Birmingham, UK
| | - Animesh Acharjee
- Health Data Research UK Midlands, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Institute of Cancer and Genomic Sciences, University of Birmingham, Vincent Drive, B15 2TT Birmingham, UK
| | - Jinming Duan
- School of Computer Science, University of Birmingham, Birmingham, UK
- Alan Turing Institute, London, UK
| | - Lorenzo Dall'Olio
- Department of Physics and Astronomy, University of Bologna, Bologna, Italy
| | - Said el Bouhaddani
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Saisakul Chernbumroong
- Health Data Research UK Midlands, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Institute of Cancer and Genomic Sciences, University of Birmingham, Vincent Drive, B15 2TT Birmingham, UK
| | | | | | - Folkert W Asselbergs
- Amsterdam University Medical Center, Department of Cardiology, University of Amsterdam, Amsterdam, The Netherlands
- Health Data Research UK and Institute of Health Informatics, University College London, London, UK
| | - Diederick E Grobbee
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Marinus J C Eijkemans
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Georgios V Gkoutos
- Health Data Research UK Midlands, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Institute of Cancer and Genomic Sciences, University of Birmingham, Vincent Drive, B15 2TT Birmingham, UK
| | - Dipak Kotecha
- Institute of Cardiovascular Sciences, University of Birmingham, Vincent Drive, B15 2TT Birmingham, UK
- Health Data Research UK Midlands, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
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13
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Krefting J, Sen P, David-Rus D, Güldener U, Hawe JS, Cassese S, von Scheidt M, Schunkert H. Use of big data from health insurance for assessment of cardiovascular outcomes. Front Artif Intell 2023; 6:1155404. [PMID: 37207237 PMCID: PMC10188985 DOI: 10.3389/frai.2023.1155404] [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: 02/24/2023] [Accepted: 04/13/2023] [Indexed: 05/21/2023] Open
Abstract
Outcome research that supports guideline recommendations for primary and secondary preventions largely depends on the data obtained from clinical trials or selected hospital populations. The exponentially growing amount of real-world medical data could enable fundamental improvements in cardiovascular disease (CVD) prediction, prevention, and care. In this review we summarize how data from health insurance claims (HIC) may improve our understanding of current health provision and identify challenges of patient care by implementing the perspective of patients (providing data and contributing to society), physicians (identifying at-risk patients, optimizing diagnosis and therapy), health insurers (preventive education and economic aspects), and policy makers (data-driven legislation). HIC data has the potential to inform relevant aspects of the healthcare systems. Although HIC data inherit limitations, large sample sizes and long-term follow-up provides enormous predictive power. Herein, we highlight the benefits and limitations of HIC data and provide examples from the cardiovascular field, i.e. how HIC data is supporting healthcare, focusing on the demographical and epidemiological differences, pharmacotherapy, healthcare utilization, cost-effectiveness and outcomes of different treatments. As an outlook we discuss the potential of using HIC-based big data and modern artificial intelligence (AI) algorithms to guide patient education and care, which could lead to the development of a learning healthcare system and support a medically relevant legislation in the future.
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Affiliation(s)
- Johannes Krefting
- Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, Munich, Germany
- German Center for Cardiovascular Research e.V. (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
- *Correspondence: Johannes Krefting
| | - Partho Sen
- Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, Munich, Germany
| | - Diana David-Rus
- Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, Munich, Germany
| | - Ulrich Güldener
- Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, Munich, Germany
| | - Johann S. Hawe
- Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, Munich, Germany
| | - Salvatore Cassese
- Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, Munich, Germany
- German Center for Cardiovascular Research e.V. (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Moritz von Scheidt
- Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, Munich, Germany
- German Center for Cardiovascular Research e.V. (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Heribert Schunkert
- Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, Munich, Germany
- German Center for Cardiovascular Research e.V. (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
- Heribert Schunkert
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14
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Gao J, Sarwar Z. How do firms create business value and dynamic capabilities by leveraging big data analytics management capability? INFORMATION TECHNOLOGY & MANAGEMENT 2022:1-22. [PMID: 36267115 PMCID: PMC9569419 DOI: 10.1007/s10799-022-00380-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/21/2022] [Indexed: 12/03/2022]
Abstract
Despite researchers having averred that big data analytics (BDA) transforms firms' ways of doing business, knowledge about operationalizing these technologies in organizations to achieve strategic objectives is lacking. Moreover, organizations' great appetite for big data and limited empirical proof of whether BDA impacts organizations' transformational capacity poses a need for further empirical investigation. Therefore, this study explores the association between big data analytics management capabilities (BDAMC) and innovation performance via dynamic capabilities (DC), by applying the PLS-SEM technique to analyzing the feedback of 149 firms. Consequently, we ground our arguments on dynamic capability and social capital theory rather than a resource-based view that does not provide suitable explanations for the deployment of resources to adapt to change. Accordingly, we advance this research stream by finding that BDAMC significantly enhances innovation performance through DC. We also extend the literature by disclosing how BDAMC strengthens DC via strategic alignment and social capital.
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Affiliation(s)
- Jingmei Gao
- School of Business Administration, Dongbei University of Finance and Economics, Dalian, 116025 People’s Republic of China
| | - Zahid Sarwar
- School of Business Administration, Dongbei University of Finance and Economics, Dalian, 116025 People’s Republic of China
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15
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Hassan CAU, Iqbal J, Irfan R, Hussain S, Algarni AD, Bukhari SSH, Alturki N, Ullah SS. Effectively Predicting the Presence of Coronary Heart Disease Using Machine Learning Classifiers. SENSORS (BASEL, SWITZERLAND) 2022; 22:7227. [PMID: 36236325 PMCID: PMC9573101 DOI: 10.3390/s22197227] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 06/03/2022] [Accepted: 07/27/2022] [Indexed: 06/16/2023]
Abstract
Coronary heart disease is one of the major causes of deaths around the globe. Predicating a heart disease is one of the most challenging tasks in the field of clinical data analysis. Machine learning (ML) is useful in diagnostic assistance in terms of decision making and prediction on the basis of the data produced by healthcare sector globally. We have also perceived ML techniques employed in the medical field of disease prediction. In this regard, numerous research studies have been shown on heart disease prediction using an ML classifier. In this paper, we used eleven ML classifiers to identify key features, which improved the predictability of heart disease. To introduce the prediction model, various feature combinations and well-known classification algorithms were used. We achieved 95% accuracy with gradient boosted trees and multilayer perceptron in the heart disease prediction model. The Random Forest gives a better performance level in heart disease prediction, with an accuracy level of 96%.
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Affiliation(s)
- Ch. Anwar ul Hassan
- Department of Creative Technologies, Air University Islamabad, Islamabad 44000, Pakistan
| | - Jawaid Iqbal
- Department of Computer Science, Capital University of Science and Technology, Islamabad 44000, Pakistan
| | - Rizwana Irfan
- Department of Computer Science, University of Jeddah, P.O. Box 123456, Jeddah 21959, Saudi Arabia
| | - Saddam Hussain
- School of Digital Science, Universiti Brunei Darussalam, Jalan Tungku Link, Gadong BE1410, Brunei
| | - Abeer D. Algarni
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | | | - Nazik Alturki
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Syed Sajid Ullah
- Department of Information and Communication Technology, University of Agder (UiA), N-4898 Grimstad, Norway
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16
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Batran A, Al-Humran SM, Malak MZ, Ayed A. The Relationship Between Nursing Informatics Competency and Clinical Decision-Making Among Nurses in West Bank, Palestine. Comput Inform Nurs 2022; 40:547-553. [PMID: 35234705 DOI: 10.1097/cin.0000000000000890] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
This study aimed to examine the relationship between nursing informatics competencies and clinical decision-making by taking into account nurses' individual characteristics and job-related characteristics. A cross-sectional design was used. The cluster random sampling method was adopted to select 14 governmental hospitals in West Bank, Palestine, in which all nurses in these hospitals were invited to participate in this study. Results found that the total mean (SD) score for the nursing informatics competency scale was 2.6 (0.88), which indicates that the nurses had lower nursing informatics competency, and the informatics skills subscale had the lowest mean score (mean [SD], 2.4 [1.00]). Concerning clinical decision-making, the total mean (SD) score was 2.59 (0.38), which indicates that the nurses had lower clinical decision-making. Regarding clinical decision-making subscales, searching for information and unbiased assimilation of new information had the highest mean score (mean [SD], 2.64 [0.39]); on the contrary, the canvassing of objectives and values subscale had the lowest mean score (mean [SD], 2.53 [0.38]). Nursing informatics competency had a positive relationship with clinical decision-making. Thus, it is necessary to enhance nurses' informatics competency, especially informatics skills and clinical decision-making, by developing training programs about this technology directed to nurses.
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Affiliation(s)
- Ahmad Batran
- Author Affiliations: Pediatric Health Nursing, Faculty of Nursing, Faculty of Allied Medical Sciences, Department of Nursing, Palestine Ahliya University, Palestine (Dr Batran); Pediatric Health Nursing, Faculty of Nursing (Dr Ayed), and Deanship of Admission and Registration (Mr Al-Humran), Arab American University, Jenin, Palestine; and Community Health Nursing, Faculty of Nursing, Al-Zaytoonah University of Jordan (Dr Malak), Amman
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Towards the Use of Big Data in Healthcare: A Literature Review. Healthcare (Basel) 2022; 10:healthcare10071232. [PMID: 35885759 PMCID: PMC9322051 DOI: 10.3390/healthcare10071232] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Revised: 06/23/2022] [Accepted: 06/29/2022] [Indexed: 12/13/2022] Open
Abstract
The interest in new and more advanced technological solutions is paving the way for the diffusion of innovative and revolutionary applications in healthcare organizations. The application of an artificial intelligence system to medical research has the potential to move toward highly advanced e-Health. This analysis aims to explore the main areas of application of big data in healthcare, as well as the restructuring of the technological infrastructure and the integration of traditional data analytical tools and techniques with an elaborate computational technology that is able to enhance and extract useful information for decision-making. We conducted a literature review using the Scopus database over the period 2010–2020. The article selection process involved five steps: the planning and identification of studies, the evaluation of articles, the extraction of results, the summary, and the dissemination of the audit results. We included 93 documents. Our results suggest that effective and patient-centered care cannot disregard the acquisition, management, and analysis of a huge volume and variety of health data. In this way, an immediate and more effective diagnosis could be possible while maximizing healthcare resources. Deriving the benefits associated with digitization and technological innovation, however, requires the restructuring of traditional operational and strategic processes, and the acquisition of new skills.
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Ge J, Kim WR, Lai JC, Kwong AJ. "Beyond MELD" - Emerging strategies and technologies for improving mortality prediction, organ allocation and outcomes in liver transplantation. J Hepatol 2022; 76:1318-1329. [PMID: 35589253 DOI: 10.1016/j.jhep.2022.03.003] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 02/24/2022] [Accepted: 03/04/2022] [Indexed: 02/06/2023]
Abstract
In this review article, we discuss the model for end-stage liver disease (MELD) score and its dual purpose in general and transplant hepatology. As the landscape of liver disease and transplantation has evolved considerably since the advent of the MELD score, we summarise emerging concepts, methodologies, and technologies that may improve mortality prognostication in the future. Finally, we explore how these novel concepts and technologies may be incorporated into clinical practice.
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Affiliation(s)
- Jin Ge
- Division of Gastroenterology and Hepatology, Department of Medicine, University of California - San Francisco, San Francisco, CA, USA
| | - W Ray Kim
- Division of Gastroenterology and Hepatology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
| | - Jennifer C Lai
- Division of Gastroenterology and Hepatology, Department of Medicine, University of California - San Francisco, San Francisco, CA, USA
| | - Allison J Kwong
- Division of Gastroenterology and Hepatology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
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Greenberg JK, Otun A, Ghogawala Z, Yen PY, Molina CA, Limbrick DD, Foraker RE, Kelly MP, Ray WZ. Translating Data Analytics Into Improved Spine Surgery Outcomes: A Roadmap for Biomedical Informatics Research in 2021. Global Spine J 2022; 12:952-963. [PMID: 33973491 PMCID: PMC9344511 DOI: 10.1177/21925682211008424] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/29/2023] Open
Abstract
STUDY DESIGN Narrative review. OBJECTIVES There is growing interest in the use of biomedical informatics and data analytics tools in spine surgery. Yet despite the rapid growth in research on these topics, few analytic tools have been implemented in routine spine practice. The purpose of this review is to provide a health information technology (HIT) roadmap to help translate data assets and analytics tools into measurable advances in spine surgical care. METHODS We conducted a narrative review of PubMed and Google Scholar to identify publications discussing data assets, analytical approaches, and implementation strategies relevant to spine surgery practice. RESULTS A variety of data assets are available for spine research, ranging from commonly used datasets, such as administrative billing data, to emerging resources, such as mobile health and biobanks. Both regression and machine learning techniques are valuable for analyzing these assets, and researchers should recognize the particular strengths and weaknesses of each approach. Few studies have focused on the implementation of HIT, and a variety of methods exist to help translate analytic tools into clinically useful interventions. Finally, a number of HIT-related challenges must be recognized and addressed, including stakeholder acceptance, regulatory oversight, and ethical considerations. CONCLUSIONS Biomedical informatics has the potential to support the development of new HIT that can improve spine surgery quality and outcomes. By understanding the development life-cycle that includes identifying an appropriate data asset, selecting an analytic approach, and leveraging an effective implementation strategy, spine researchers can translate this potential into measurable advances in patient care.
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Affiliation(s)
- Jacob K. Greenberg
- Department of Neurological Surgery, Washington University School of Medicine,
St. Louis, MO, USA,Jacob K. Greenberg, Department of
Neurosurgery, Washington University School of Medicine, 660S. Euclid Ave., Box
8057, St. Louis, MO 63 110, USA.
| | - Ayodamola Otun
- Department of Neurological Surgery, Washington University School of Medicine,
St. Louis, MO, USA
| | - Zoher Ghogawala
- Department of Neurosurgery, Lahey Hospital and Medical Center, Burlington, MA, USA
| | - Po-Yin Yen
- Institute for Informatics, Washington University School of Medicine,
St. Louis, MO, USA
| | - Camilo A. Molina
- Department of Neurological Surgery, Washington University School of Medicine,
St. Louis, MO, USA
| | - David D. Limbrick
- Department of Neurological Surgery, Washington University School of Medicine,
St. Louis, MO, USA
| | - Randi E Foraker
- Institute for Informatics, Washington University School of Medicine,
St. Louis, MO, USA
| | - Michael P. Kelly
- Department of Orthopaedic Surgery, Washington University School of Medicine,
St. Louis, MO, USA
| | - Wilson Z. Ray
- Department of Neurological Surgery, Washington University School of Medicine,
St. Louis, MO, USA
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20
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Nwebonyi N, Silva S, de Freitas C. Public Views About Involvement in Decision-Making on Health Data Sharing, Access, Use and Reuse: The Importance of Trust in Science and Other Institutions. Front Public Health 2022; 10:852971. [PMID: 35619806 PMCID: PMC9127133 DOI: 10.3389/fpubh.2022.852971] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 03/28/2022] [Indexed: 11/24/2022] Open
Abstract
Background Data-intensive and needs-driven research can deliver substantial health benefits. However, concerns with privacy loss, undisclosed surveillance, and discrimination are on the rise due to mounting data breaches. This can undermine the trustworthiness of data processing institutions and reduce people's willingness to share their data. Involving the public in health data governance can help to address this problem by imbuing data processing frameworks with societal values. This study assesses public views about involvement in individual-level decisions concerned with health data and their association with trust in science and other institutions. Methods Cross-sectional study with 162 patients and 489 informal carers followed at two reference centers for rare diseases in an academic hospital in Portugal (June 2019–March 2020). Participants rated the importance of involvement in decision-making concerning health data sharing, access, use, and reuse from “not important” to “very important”. Its association with sociodemographic characteristics, interpersonal trust, trust in national and international institutions, and the importance of trust in research teams and host institutions was tested. Results Most participants perceived involvement in decision-making about data sharing (85.1%), access (87.1%), use (85%) and reuse (79.9%) to be important or very important. Participants who ascribed a high degree of importance to trust in research host institutions were significantly more likely to value involvement in such decisions. A similar position was expressed by participants who valued trust in research teams for data sharing, access, and use. Participants with low levels of trust in national and international institutions and with lower levels of education attributed less importance to being involved in decisions about data use. Conclusion The high value attributed by participants to involvement in individual-level data governance stresses the need to broaden opportunities for public participation in health data decision-making, namely by introducing a meta consent approach. The important role played by trust in science and in other institutions in shaping participants' views about involvement highlights the relevance of pairing such a meta consent approach with the provision of transparent information about the implications of data sharing, the resources needed to make informed choices and the development of harm mitigation tools and redress.
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Affiliation(s)
- Ngozi Nwebonyi
- Laboratório para a Investigação Integrativa e Translacional em Saúde Populacional (ITR), Porto, Portugal.,Departamento de Ciências da Saúde Pública e Forenses e Educação Médica, Faculdade de Medicina da Universidade do Porto (FMUP), Porto, Portugal
| | - Susana Silva
- Departamento de Sociologia, Instituto de Ciências Sociais, Universidade do Minho, Braga, Portugal.,Centro em Rede de Investigação em Antropologia, Universidade do Minho, Braga, Portugal
| | - Cláudia de Freitas
- Laboratório para a Investigação Integrativa e Translacional em Saúde Populacional (ITR), Porto, Portugal.,Departamento de Ciências da Saúde Pública e Forenses e Educação Médica, Faculdade de Medicina da Universidade do Porto (FMUP), Porto, Portugal.,EPIUnit - Instituto de Saúde Pública, Universidade do Porto, Porto, Portugal.,Centre for Research and Studies in Sociology, University Institute of Lisbon (ISCTE-IUL), Lisbon, Portugal
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21
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Brown SA, Hudson C, Hamid A, Berman G, Echefu G, Lee K, Lamberg M, Olson J. The pursuit of health equity in digital transformation, health informatics, and the cardiovascular learning healthcare system. AMERICAN HEART JOURNAL PLUS : CARDIOLOGY RESEARCH AND PRACTICE 2022; 17:100160. [PMID: 38559893 PMCID: PMC10978355 DOI: 10.1016/j.ahjo.2022.100160] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 06/20/2022] [Accepted: 06/22/2022] [Indexed: 04/04/2024]
Abstract
African Americans have a higher rate of cardiovascular morbidity and mortality and a lower rate of specialty consultation and treatment than Caucasians. These disparities also exist in the care and treatment of chemotherapy-related cardiovascular complications. African Americans suffer from cardiotoxicity at a higher rate than Caucasians and are underrepresented in clinical trials aimed at preventing cardiovascular injury associated with cancer therapies. To eliminate racial and ethnic disparities in the prevention of cardiotoxicity, an interdisciplinary and innovative approach will be required. Diverse forms of digital transformation leveraging health informatics have the potential to contribute to health equity if they are implemented carefully and thoughtfully in collaboration with minority communities. A learning healthcare system can serve as a model for developing, deploying, and disseminating interventions to minimize health inequities and maximize beneficial impact.
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Affiliation(s)
- Sherry-Ann Brown
- Cardio-Oncology Program, Division of Cardiovascular Medicine, Medical College of Wisconsin, Milwaukee, WI, USA
| | | | | | | | - Gift Echefu
- Baton Rouge General Medical Center, Department of Internal Medicine, Baton Rouge, LA, USA
| | - Kyla Lee
- Tulane School of Public Health and Tropical Medicine, New Orleans, LA, USA
| | - Morgan Lamberg
- Department of Medicine, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Jessica Olson
- Institute for Health & Equity, Medical College of Wisconsin, Milwaukee, WI, USA
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22
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How can Big Data Analytics Support People-Centred and Integrated Health Services: A Scoping Review. Int J Integr Care 2022; 22:23. [PMID: 35756337 PMCID: PMC9205381 DOI: 10.5334/ijic.5543] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Accepted: 06/08/2022] [Indexed: 01/16/2023] Open
Abstract
Introduction: Health systems in high-income countries face a variety of challenges calling for a systemic approach to improve quality and efficiency. Putting people in the centre is the main idea of the WHO model of people-centred and integrated health services. Integrating health services is fuelled by an integration of health data with great potentials for decision support based on big data analytics. The research question of this paper is “How can big data analytics support people-centred and integrated health services?” Methods: A scoping review following the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses – Scoping Review (PRISMA-ScR) statement was conducted to gather information on how big data analytics can support people-centred and integrated health services. The results were summarized in a role model of a people-centred and integrated health services platform illustrating which data sources might be integrated and which types of analytics might be applied to support the strategies of the people-centred and integrated health services framework to become more integrated across the continuum of care. Additional rapid literature reviews were conducted to generate frequency distributions of the most often used data types and analytical methods in the medical literature. Finally, the main challenges connected with big data analytics were worked out based on a content analysis of the results from the scoping literature review. Results: Based on the results from the rapid literature reviews the most often used data sources for big data analytics (BDA) in healthcare were biomarkers (39.3%) and medical images (30.9%). The most often used analytical models were support vector machines (27.3%) and neural networks (20.4%). The people-centred and integrated health services framework defines different strategic interventions for health services to become more integrated. To support all aspects of these interventions a comparably integrated platform of health-related data would be needed, so that a role model labelled as people-centred health platform was developed. Based on integrated data the results of the scoping review (n = 72) indicate, that big data analytics could for example support the strategic intervention of tailoring personalized health plans (43.1%), e.g. by predicting individual risk factors for different therapy options. Also BDA might enhance clinical decision support tools (31.9%), e.g. by calculating risk factors for disease uptake or progression. BDA might also assist in designing population-based services (26.4% by clustering comparable individuals in manageable risk groups e.g. mentored by specifically trained, non-medical professionals. The main challenges of big data analytics in healthcare were categorized in regulatory, (information-) technological, methodological, and cultural issues, whereas methodological challenges were mentioned most often (55.0%), followed by regulatory challenges (43.7%). Discussion: The BDA applications presented in this literature review are based on findings which have already been published. For some important components of the framework on people-centred care like enhancing the role of community care or establishing intersectoral partnerships between health and social care institutions only few examples of enabling big data analytical tools were found in the literature. Quite the opposite does this mean that these strategies have less potential value, but rather that the source systems in these fields need to be further developed to be suitable for big data analytics. Conclusions: Big data analytics can support people-centred and integrated health services e.g. by patient similarity stratifications or predictions of individual risk factors. But BDA fails to unfold its full potential until data source systems are still disconnected and actions towards a comprehensive and people-centred health-related data platform are politically insufficiently incentivized. This work highlighted the potential of big data analysis in the context of the model of people-centred and integrated health services, whereby the role model of the person-centered health platform can be used as a blueprint to support strategies to improve person-centered health care. Likely because health data is extremely sensitive and complex, there are only few practical examples of platforms to some extent already capable of merging and processing people-centred big data, but the integration of health data can be expected to further proceed so that analytical opportunities might also become reality in the near future.
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Dai H, Younis A, Kong JD, Puce L, Jabbour G, Yuan H, Bragazzi NL. Big Data in Cardiology: State-of-Art and Future Prospects. Front Cardiovasc Med 2022; 9:844296. [PMID: 35433868 PMCID: PMC9010556 DOI: 10.3389/fcvm.2022.844296] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Accepted: 02/24/2022] [Indexed: 11/23/2022] Open
Abstract
Cardiological disorders contribute to a significant portion of the global burden of disease. Cardiology can benefit from Big Data, which are generated and released by different sources and channels, like epidemiological surveys, national registries, electronic clinical records, claims-based databases (epidemiological Big Data), wet-lab, and next-generation sequencing (molecular Big Data), smartphones, smartwatches, and other mobile devices, sensors and wearable technologies, imaging techniques (computational Big Data), non-conventional data streams such as social networks, and web queries (digital Big Data), among others. Big Data is increasingly having a more and more relevant role, being highly ubiquitous and pervasive in contemporary society and paving the way for new, unprecedented perspectives in biomedicine, including cardiology. Big Data can be a real paradigm shift that revolutionizes cardiological practice and clinical research. However, some methodological issues should be properly addressed (like recording and association biases) and some ethical issues should be considered (such as privacy). Therefore, further research in the field is warranted.
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Affiliation(s)
- Haijiang Dai
- Department of Cardiology, The Third Xiangya Hospital, Central South University, Changsha, China
- Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, ON, Canada
| | - Arwa Younis
- Clinical Cardiovascular Research Center, University of Rochester Medical Center, Rochester, New York, NY, United States
| | - Jude Dzevela Kong
- Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, ON, Canada
| | - Luca Puce
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
| | - Georges Jabbour
- Physical Education Department, College of Education, Qatar University, Doha, Qatar
| | - Hong Yuan
- Department of Cardiology, The Third Xiangya Hospital, Central South University, Changsha, China
- Hong Yuan
| | - Nicola Luigi Bragazzi
- Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, ON, Canada
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
- Postgraduate School of Public Health, Department of Health Sciences, University of Genoa, Genoa, Italy
- Section of Musculoskeletal Disease, Leeds Institute of Molecular Medicine, NIHR Leeds Musculoskeletal Biomedical Research Unit, University of Leeds, Chapel Allerton Hospital, Leeds, United Kingdom
- *Correspondence: Nicola Luigi Bragazzi
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Curtis AB, Manrodt C, Jacobsen LD, Soderlund D, Fonarow GC. Guideline-directed device therapies in heart failure: A clinical practice-based analysis using electronic health record data. AMERICAN HEART JOURNAL PLUS : CARDIOLOGY RESEARCH AND PRACTICE 2022; 16:100139. [PMID: 38559281 PMCID: PMC10976280 DOI: 10.1016/j.ahjo.2022.100139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 04/13/2022] [Accepted: 04/26/2022] [Indexed: 04/04/2024]
Abstract
Background Guideline-directed device therapies (GDDT) improve outcomes for eligible patients with heart failure (HF) with reduced ejection fraction (HFrEF). Utilization rates of device therapies in HFrEF after the 2012 ACCF/AHA/HRS Focused Update for Device-based Therapies of Cardiac Rhythm Abnormalities have not been well studied. Objective Characterize the use of GDDT in newly indicated HFrEF patients from 2012 to 2019 using aggregated electronic health record (EHR) data. Methods Computable phenotyping algorithms for implantable cardioverter defibrillator/cardiac resynchronization therapy-defibrillator (ICD/CRT-D) indications from the GuideLine Indications Detected in EHR for Heart Failure program (GLIDE-HF) used diagnoses, procedures, measures, prescriptions, and the output of natural language processed provider notes from de-identified Optum® EHR data. Patients had a diagnosis of HF, dilated cardiomyopathy, or prior infarct, and were included if they had HFrEF with >1 year of records prior to a new Class 1 or Class 2a indication for an ICD or cardiac resynchronization therapy with defibrillator (CRT-D) from 2012 to 2019. Results Records showed 137,476 HFrEF patients were newly indicated for an ICD or CRT-D. GDDT was used in 14,892 of 36,358 (41.0%) CRT-D indicated patients and in 14,904 of 101,118 (14.7%) ICD-indicated patients. While GDDT use was low, 95.7% had echocardiography and 92.1% had prescriptions for beta-blockers or angiotensin-converting enzyme/angiotensin-receptor blockers medications. Conclusions In this modern cohort of HF patients, a large proportion of eligible patients did not receive ICDs or CRT-Ds, while frequently receiving other indicated cardiovascular interventions and treatments.
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Affiliation(s)
- Anne B. Curtis
- Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, United States of America
| | | | | | - Dana Soderlund
- Medtronic, Inc., Mounds View, MS, United States of America
| | - Gregg C. Fonarow
- Ahmanson-UCLA Cardiomyopathy Center, Ronald Reagan UCLA Medical Center, Los Angeles, CA, United States of America
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Predicting Parkinson’s Disease Progression: Evaluation of Ensemble Methods in Machine Learning. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:2793361. [PMID: 35154618 PMCID: PMC8831050 DOI: 10.1155/2022/2793361] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Revised: 01/13/2022] [Accepted: 01/15/2022] [Indexed: 01/12/2023]
Abstract
Parkinson’s disease (PD) is a complex neurodegenerative disease. Accurate diagnosis of this disease in the early stages is crucial for its initial treatment. This paper aims to present a comparative study on the methods developed by machine learning techniques in PD diagnosis. We rely on clustering and prediction learning approaches to perform the comparative study. Specifically, we use different clustering techniques for PD data clustering and support vector regression ensembles to predict Motor-UPDRS and Total-UPDRS. The results are then compared with the other prediction learning approaches, multiple linear regression, neurofuzzy, and support vector regression techniques. The comparative study is performed on a real-world PD dataset. The prediction results of data analysis on a PD real-world dataset revealed that expectation-maximization with the aid of SVR ensembles can provide better prediction accuracy in relation to decision trees, deep belief network, neurofuzzy, and support vector regression combined with other clustering techniques in the prediction of Motor-UPDRS and Total-UPDRS.
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John Cremin C, Dash S, Huang X. Big Data: Historic Advances and Emerging Trends in Biomedical Research. CURRENT RESEARCH IN BIOTECHNOLOGY 2022. [DOI: 10.1016/j.crbiot.2022.02.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022] Open
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Different Scales of Medical Data Classification Based on Machine Learning Techniques: A Comparative Study. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12020919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
In recent years, medical data have vastly increased due to the continuous generation of digital data. The different forms of medical data, such as reports, textual, numerical, monitoring, and laboratory data generate the so-called medical big data. This paper aims to find the best algorithm which predicts new medical data with high accuracy, since good prediction accuracy is essential in medical fields. To achieve the study’s goal, the best accuracy algorithm and least processing time algorithm are defined through an experiment and comparison of seven different algorithms, including Naïve bayes, linear model, regression, decision tree, random forest, gradient boosted tree, and J48. The conducted experiments have allowed the prediction of new medical big data that reach the algorithm with the best accuracy and processing time. Here, we find that the best accuracy classification algorithm is the random forest with accuracy values of 97.58%, 83.59%, and 90% for heart disease, M-health, and diabetes datasets, respectively. The Naïve bayes has the lowest processing time with values of 0.078, 7.683, and 22.374 s for heart disease, M-health, and diabetes datasets, respectively. In addition, the best result of the experiment is obtained by the combination of the CFS feature selection algorithm with the Random Forest classification algorithm. The results of applying RF with the combination of CFS on the heart disease dataset are as follows: Accuracy of 90%, precision of 83.3%, sensitivity of 100, and consuming time of 3 s. Moreover, the results of applying this combination on the M-health dataset are as follows: Accuracy of 83.59%, precision of 74.3%, sensitivity of 93.1, and consuming time of 13.481 s. Furthermore, the results on the diabetes dataset are as follows: Accuracy of 97.58%, precision of 86.39%, sensitivity of 97.14, and consuming time of 56.508 s.
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Batko K, Ślęzak A. The use of Big Data Analytics in healthcare. JOURNAL OF BIG DATA 2022; 9:3. [PMID: 35013701 PMCID: PMC8733917 DOI: 10.1186/s40537-021-00553-4] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2021] [Accepted: 12/19/2021] [Indexed: 05/30/2023]
Abstract
The introduction of Big Data Analytics (BDA) in healthcare will allow to use new technologies both in treatment of patients and health management. The paper aims at analyzing the possibilities of using Big Data Analytics in healthcare. The research is based on a critical analysis of the literature, as well as the presentation of selected results of direct research on the use of Big Data Analytics in medical facilities. The direct research was carried out based on research questionnaire and conducted on a sample of 217 medical facilities in Poland. Literature studies have shown that the use of Big Data Analytics can bring many benefits to medical facilities, while direct research has shown that medical facilities in Poland are moving towards data-based healthcare because they use structured and unstructured data, reach for analytics in the administrative, business and clinical area. The research positively confirmed that medical facilities are working on both structural data and unstructured data. The following kinds and sources of data can be distinguished: from databases, transaction data, unstructured content of emails and documents, data from devices and sensors. However, the use of data from social media is lower as in their activity they reach for analytics, not only in the administrative and business but also in the clinical area. It clearly shows that the decisions made in medical facilities are highly data-driven. The results of the study confirm what has been analyzed in the literature that medical facilities are moving towards data-based healthcare, together with its benefits.
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Affiliation(s)
- Kornelia Batko
- Department of Business Informatics, University of Economics in Katowice, Katowice, Poland
| | - Andrzej Ślęzak
- Department of Biomedical Processes and Systems, Institute of Health and Nutrition Sciences, Częstochowa University of Technology, Częstochowa, Poland
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Iyamu I, Gómez-Ramírez O, Xu AXT, Chang HJ, Watt S, Mckee G, Gilbert M. Challenges in the development of digital public health interventions and mapped solutions: Findings from a scoping review. Digit Health 2022; 8:20552076221102255. [PMID: 35656283 PMCID: PMC9152201 DOI: 10.1177/20552076221102255] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023] Open
Abstract
Background “Digital public health” has emerged from an interest in integrating digital technologies into public health. However, significant challenges which limit the scale and extent of this digital integration in various public health domains have been described. We summarized the literature about these challenges and identified strategies to overcome them. Methods We adopted Arksey and O’Malley's framework (2005) integrating adaptations by Levac et al. (2010). OVID Medline, Embase, Google Scholar, and 14 government and intergovernmental agency websites were searched using terms related to “digital” and “public health.” We included conceptual and explicit descriptions of digital technologies in public health published in English between 2000 and June 2020. We excluded primary research articles about digital health interventions. Data were extracted using a codebook created using the European Public Health Association's conceptual framework for digital public health. Results and analysis Overall, 163 publications were included from 6953 retrieved articles with the majority (64%, n = 105) published between 2015 and June 2020. Nontechnical challenges to digital integration in public health concerned ethics, policy and governance, health equity, resource gaps, and quality of evidence. Technical challenges included fragmented and unsustainable systems, lack of clear standards, unreliability of available data, infrastructure gaps, and workforce capacity gaps. Identified strategies included securing political commitment, intersectoral collaboration, economic investments, standardized ethical, legal, and regulatory frameworks, adaptive research and evaluation, health workforce capacity building, and transparent communication and public engagement. Conclusion Developing and implementing digital public health interventions requires efforts that leverage identified strategies to overcome diverse challenges encountered in integrating digital technologies in public health.
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Affiliation(s)
- Ihoghosa Iyamu
- School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
- British Columbia Centre for Disease Control, Vancouver, BC, Canada
| | - Oralia Gómez-Ramírez
- School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
- British Columbia Centre for Disease Control, Vancouver, BC, Canada
- CIHR Canadian HIV Trials Network, Vancouver, BC, Canada
| | - Alice XT Xu
- School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
| | - Hsiu-Ju Chang
- British Columbia Centre for Disease Control, Vancouver, BC, Canada
| | - Sarah Watt
- British Columbia Centre for Disease Control, Vancouver, BC, Canada
| | - Geoff Mckee
- British Columbia Centre for Disease Control, Vancouver, BC, Canada
| | - Mark Gilbert
- School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
- British Columbia Centre for Disease Control, Vancouver, BC, Canada
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30
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E E, Carey JJ, Wang T, Yang L, Chan WP, Whelan B, Silke C, O'Sullivan M, Rooney B, McPartland A, O'Malley G, Brennan A, Yu M, Dempsey M. Conceptual design of the dual X-ray absorptiometry health informatics prediction system for osteoporosis care. Health Informatics J 2022; 28:14604582211066465. [PMID: 35257612 DOI: 10.1177/14604582211066465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Osteoporotic fractures are a major and growing public health problem, which is strongly associated with other illnesses and multi-morbidity. Big data analytics has the potential to improve care for osteoporotic fractures and other non-communicable diseases (NCDs), reduces healthcare costs and improves healthcare decision-making for patients with multi-disorders. However, robust and comprehensive utilization of healthcare big data in osteoporosis care practice remains unsatisfactory. In this paper, we present a conceptual design of an intelligent analytics system, namely, the dual X-ray absorptiometry (DXA) health informatics prediction (HIP) system, for healthcare big data research and development. Comprising data source, extraction, transformation, loading, modelling and application, the DXA HIP system was applied in an osteoporosis healthcare context for fracture risk prediction and the investigation of multi-morbidity risk. Data was sourced from four DXA machines located in three healthcare centres in Ireland. The DXA HIP system is novel within the Irish context as it enables the study of fracture-related issues in a larger and more representative Irish population than previous studies. We propose this system is applicable to investigate other NCDs which have the potential to improve the overall quality of patient care and substantially reduce the burden and cost of all NCDs.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | - Attracta Brennan
- Department of Industrial Engineering, Tsinghua University, Beijing, China
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31
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Van den Eynde J, Manlhiot C, Van De Bruaene A, Diller GP, Frangi AF, Budts W, Kutty S. Medicine-Based Evidence in Congenital Heart Disease: How Artificial Intelligence Can Guide Treatment Decisions for Individual Patients. Front Cardiovasc Med 2021; 8:798215. [PMID: 34926630 PMCID: PMC8674499 DOI: 10.3389/fcvm.2021.798215] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 11/09/2021] [Indexed: 01/06/2023] Open
Abstract
Built on the foundation of the randomized controlled trial (RCT), Evidence Based Medicine (EBM) is at its best when optimizing outcomes for homogeneous cohorts of patients like those participating in an RCT. Its weakness is a failure to resolve a clinical quandary: patients appear for care individually, each may differ in important ways from an RCT cohort, and the physician will wonder each time if following EBM will provide best guidance for this unique patient. In an effort to overcome this weakness, and promote higher quality care through a more personalized approach, a new framework has been proposed: Medicine-Based Evidence (MBE). In this approach, big data and deep learning techniques are embraced to interrogate treatment responses among patients in real-world clinical practice. Such statistical models are then integrated with mechanistic disease models to construct a “digital twin,” which serves as the real-time digital counterpart of a patient. MBE is thereby capable of dynamically modeling the effects of various treatment decisions in the context of an individual's specific characteristics. In this article, we discuss how MBE could benefit patients with congenital heart disease, a field where RCTs are difficult to conduct and often fail to provide definitive solutions because of a small number of subjects, their clinical complexity, and heterogeneity. We will also highlight the challenges that must be addressed before MBE can be embraced in clinical practice and its full potential can be realized.
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Affiliation(s)
- Jef Van den Eynde
- Department of Cardiovascular Sciences, KU Leuven and Congenital and Structural Cardiology, UZ Leuven, Leuven, Belgium.,Blalock-Taussig-Thomas Pediatric and Congenital Heart Center, The Johns Hopkins Hospital and School of Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Cedric Manlhiot
- Blalock-Taussig-Thomas Pediatric and Congenital Heart Center, The Johns Hopkins Hospital and School of Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Alexander Van De Bruaene
- Department of Cardiovascular Sciences, KU Leuven and Congenital and Structural Cardiology, UZ Leuven, Leuven, Belgium
| | - Gerhard-Paul Diller
- Department of Cardiology III-Adult Congenital and Valvular Heart Disease, University Hospital Muenster, Muenster, Germany
| | - Alejandro F Frangi
- Department of Cardiovascular Sciences, KU Leuven and Congenital and Structural Cardiology, UZ Leuven, Leuven, Belgium.,Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), School of Computing and Medicine, University of Leeds, Leeds, United Kingdom.,Leeds Institute for Cardiovascular and Metabolic Medicine, Schools of Medicine, University of Leeds, Leeds, United Kingdom
| | - Werner Budts
- Department of Cardiovascular Sciences, KU Leuven and Congenital and Structural Cardiology, UZ Leuven, Leuven, Belgium
| | - Shelby Kutty
- Blalock-Taussig-Thomas Pediatric and Congenital Heart Center, The Johns Hopkins Hospital and School of Medicine, Johns Hopkins University, Baltimore, MD, United States
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32
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Bakker L, Aarts J, Uyl-de Groot C, Redekop K. How can we discover the most valuable types of big data and artificial intelligence-based solutions? A methodology for the efficient development of the underlying analytics that improve care. BMC Med Inform Decis Mak 2021; 21:336. [PMID: 34844594 PMCID: PMC8628451 DOI: 10.1186/s12911-021-01682-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 11/01/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Much has been invested in big data and artificial intelligence-based solutions for healthcare. However, few applications have been implemented in clinical practice. Early economic evaluations can help to improve decision-making by developers of analytics underlying these solutions aiming to increase the likelihood of successful implementation, but recommendations about their use are lacking. The aim of this study was to develop and apply a framework that positions best practice methods for economic evaluations alongside development of analytics, thereby enabling developers to identify barriers to success and to select analytics worth further investments. METHODS The framework was developed using literature, recommendations for economic evaluations and by applying the framework to use cases (chronic lymphocytic leukaemia (CLL), intensive care, diabetes). First, the feasibility of developing clinically relevant analytics was assessed and critical barriers to successful development and implementation identified. Economic evaluations were then used to determine critical thresholds and guide investment decisions. RESULTS When using the framework to assist decision-making of developers of analytics, continuing development was not always feasible or worthwhile. Developing analytics for progressive CLL and diabetes was clinically relevant but not feasible with the data available. Alternatively, developing analytics for newly diagnosed CLL patients was feasible but continuing development was not considered worthwhile because the high drug costs made it economically unattractive for potential users. Alternatively, in the intensive care unit, analytics reduced mortality and per-patient costs when used to identify infections (- 0.5%, - €886) and to improve patient-ventilator interaction (- 3%, - €264). Both analytics have the potential to save money but the potential benefits of analytics that identify infections strongly depend on infection rate; a higher rate implies greater cost-savings. CONCLUSIONS We present a framework that stimulates efficiency of development of analytics for big data and artificial intelligence-based solutions by selecting those applications of analytics for which development is feasible and worthwhile. For these applications, results from early economic evaluations can be used to guide investment decisions and identify critical requirements.
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Affiliation(s)
- Lytske Bakker
- Erasmus School of Health Policy and Management, Erasmus University, P.O. Box 1738, 3000 DR, Rotterdam, The Netherlands.
- Institute for Medical Technology Assessment, Erasmus University, Rotterdam, The Netherlands.
- Erasmus Centre for Health Economics Rotterdam (EsCHER), Erasmus University, Rotterdam, The Netherlands.
| | - Jos Aarts
- Erasmus School of Health Policy and Management, Erasmus University, P.O. Box 1738, 3000 DR, Rotterdam, The Netherlands
| | - Carin Uyl-de Groot
- Erasmus School of Health Policy and Management, Erasmus University, P.O. Box 1738, 3000 DR, Rotterdam, The Netherlands
- Institute for Medical Technology Assessment, Erasmus University, Rotterdam, The Netherlands
- Erasmus Centre for Health Economics Rotterdam (EsCHER), Erasmus University, Rotterdam, The Netherlands
| | - Ken Redekop
- Erasmus School of Health Policy and Management, Erasmus University, P.O. Box 1738, 3000 DR, Rotterdam, The Netherlands
- Institute for Medical Technology Assessment, Erasmus University, Rotterdam, The Netherlands
- Erasmus Centre for Health Economics Rotterdam (EsCHER), Erasmus University, Rotterdam, The Netherlands
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Ostrominski JW, Amione-Guerra J, Hernandez B, Michalek JE, Prasad A. Coding Variation and Adherence to Methodological Standards in Cardiac Research Using the National Inpatient Sample. Front Cardiovasc Med 2021; 8:713695. [PMID: 34796206 PMCID: PMC8592936 DOI: 10.3389/fcvm.2021.713695] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Accepted: 10/05/2021] [Indexed: 11/21/2022] Open
Abstract
Background: Code selection is crucial to the accuracy and reproducibility of studies using administrative data, however a comprehensive assessment of coding trends for major cardiac diagnoses and procedures is lacking. We aimed to evaluate trends in administrative code utilization for major cardiac diagnoses and procedures, and adherence to required methodological practices in cardiac research using the National Inpatient Sample (NIS). Methods: In this observational study of 445 articles, ICD-9-CM codes corresponding to acute myocardial infarction (AMI), heart failure, atrial fibrillation, percutaneous coronary intervention, and coronary artery bypass grafting were collected and analyzed. The NIS was used to compare the number of hospitalizations between the most frequently encountered AMI case definitions. Key elements were abstracted from each article to evaluate adherence to required methodological practices. Results: Variation in code utilization was observed for each diagnosis and procedure assessed, and the number of unique case definitions published per year increased throughout the study period (P < 0.001), driven largely by the significant increase in articles per year (P < 0.001). Off-target codes were observed in 39 (8.8%) studies. Upon reintroduction into the NIS for 2008–2012, the most commonly encountered case definitions for AMI were found to yield significantly different estimates of AMI hospitalizations and hospitalization trends over time. Three hundred and ninety-nine articles (84%) did not adhere to one or more required research practices. Overall adherence was superior for publications in higher-impact journals (P = 0.002). Conclusions: Substantial variation in code selection exists for major cardiac diagnoses and procedures, and non-adherence to methodological standards is widespread. These data have important implications for the accuracy and generalizability of analyses using the NIS.
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Affiliation(s)
- John W Ostrominski
- Department of Medicine, Division of Cardiology, UT Health San Antonio, San Antonio, TX, United States
| | - Javier Amione-Guerra
- Department of Medicine, Division of Cardiology, UT Health San Antonio, San Antonio, TX, United States
| | - Brian Hernandez
- Department of Population Health Sciences, UT Health San Antonio, San Antonio, TX, United States
| | - Joel E Michalek
- Department of Population Health Sciences, UT Health San Antonio, San Antonio, TX, United States
| | - Anand Prasad
- Department of Medicine, Division of Cardiology, UT Health San Antonio, San Antonio, TX, United States
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Ahmed Z. Intelligent health system for the investigation of consenting COVID-19 patients and precision medicine. Per Med 2021; 18:573-582. [PMID: 34619976 PMCID: PMC8544483 DOI: 10.2217/pme-2021-0068] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Advancing frontiers of clinical research, we discuss the need for intelligent health systems to support a deeper investigation of COVID-19. We hypothesize that the convergence of the healthcare data and staggering developments in artificial intelligence have the potential to elevate the recovery process with diagnostic and predictive analysis to identify major causes of mortality, modifiable risk factors and actionable information that supports the early detection and prevention of COVID-19. However, current constraints include the recruitment of COVID-19 patients for research; translational integration of electronic health records and diversified public datasets; and the development of artificial intelligence systems for data-intensive computational modeling to assist clinical decision making. We propose a novel nexus of machine learning algorithms to examine COVID-19 data granularity from population studies to subgroups stratification and ensure best modeling strategies within the data continuum.
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Affiliation(s)
- Zeeshan Ahmed
- Rutgers Institute for Health, Health Care Policy & Aging Research, Rutgers University, 112 Paterson Street, New Brunswick, NJ 08901, USA.,Department of Medicine, Robert Wood Johnson Medical School, Rutgers Biomedical & Health Sciences, 125 Paterson Street, New Brunswick, NJ 08901, USA
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35
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Christou CD, Tsoulfas G. Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology. World J Gastroenterol 2021; 27:6191-6223. [PMID: 34712027 PMCID: PMC8515803 DOI: 10.3748/wjg.v27.i37.6191] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 05/06/2021] [Accepted: 08/31/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) is an umbrella term used to describe a cluster of interrelated fields. Machine learning (ML) refers to a model that learns from past data to predict future data. Medicine and particularly gastroenterology and hepatology, are data-rich fields with extensive data repositories, and therefore fruitful ground for AI/ML-based software applications. In this study, we comprehensively review the current applications of AI/ML-based models in these fields and the opportunities that arise from their application. Specifically, we refer to the applications of AI/ML-based models in prevention, diagnosis, management, and prognosis of gastrointestinal bleeding, inflammatory bowel diseases, gastrointestinal premalignant and malignant lesions, other nonmalignant gastrointestinal lesions and diseases, hepatitis B and C infection, chronic liver diseases, hepatocellular carcinoma, cholangiocarcinoma, and primary sclerosing cholangitis. At the same time, we identify the major challenges that restrain the widespread use of these models in healthcare in an effort to explore ways to overcome them. Notably, we elaborate on the concerns regarding intrinsic biases, data protection, cybersecurity, intellectual property, liability, ethical challenges, and transparency. Even at a slower pace than anticipated, AI is infiltrating the healthcare industry. AI in healthcare will become a reality, and every physician will have to engage with it by necessity.
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Affiliation(s)
- Chrysanthos D Christou
- Organ Transplant Unit, Hippokration General Hospital, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
| | - Georgios Tsoulfas
- Organ Transplant Unit, Hippokration General Hospital, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
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36
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Deep learning-based prediction of early cerebrovascular events after transcatheter aortic valve replacement. Sci Rep 2021; 11:18754. [PMID: 34548574 PMCID: PMC8455675 DOI: 10.1038/s41598-021-98265-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Accepted: 09/06/2021] [Indexed: 11/10/2022] Open
Abstract
Cerebrovascular events (CVE) are among the most feared complications of transcatheter aortic valve replacement (TAVR). CVE appear difficult to predict due to their multifactorial origin incompletely explained by clinical predictors. We aimed to build a deep learning-based predictive tool for TAVR-related CVE. Integrated clinical and imaging characteristics from consecutive patients enrolled into a prospective TAVR registry were analysed. CVE comprised any strokes and transient ischemic attacks. Predictive variables were selected by recursive feature reduction to train an autoencoder predictive model. Area under the curve (AUC) represented the model’s performance to predict 30-day CVE. Among 2279 patients included between 2007 and 2019, both clinical and imaging data were available in 1492 patients. Median age was 83 years and STS score was 4.6%. Acute (< 24 h) and subacute (day 2–30) CVE occurred in 19 (1.3%) and 36 (2.4%) patients, respectively. The occurrence of CVE was associated with an increased risk of death (HR [95% CI] 2.62 [1.82–3.78]). The constructed predictive model uses less than 107 clinical and imaging variables and has an AUC of 0.79 (0.65–0.93). TAVR-related CVE can be predicted using a deep learning-based predictive algorithm. The model is implemented online for broad usage.
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37
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Piña IL, Allen LA, Desai NR. Policy and Payment Challenges in the Postpandemic Treatment of Heart Failure: Value-Based Care and Telehealth. J Card Fail 2021; 28:835-844. [PMID: 34520854 PMCID: PMC8434774 DOI: 10.1016/j.cardfail.2021.08.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 08/09/2021] [Accepted: 08/10/2021] [Indexed: 11/13/2022]
Abstract
Increasing patient and therapeutic complexity have created both challenges and opportunities for heart failure care. Within this background, the coronavirus disease-2019 pandemic has disrupted care as usual, accelerating the need for transition from volume-based to value-based care, and demanding a rapid expansion of telehealth and remote care for heart failure. Patients, clinicians, health systems, and payors have by necessity become more invested in these issues. Herein we review recent changes in health care policy related to the movement from volume to value-based payment and from in-person to remote care delivery.
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Affiliation(s)
- Ileana L Piña
- Central Michigan University, Mount Pleasant, Michigan.
| | - Larry A Allen
- Division of Cardiology, University of Colorado School of Medicine, Aurora, Colorado
| | - Nihar R Desai
- Department of Cardiovascular Medicine, Yale University School of Medicine, New Haven, Connecticut
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38
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Torres Roldan VD, Ponce OJ, Urtecho M, Torres GF, Belluzzo T, Montori V, Liu C, Barrera F, Diaz A, Prokop L, Guyatt G, Montori VM. Understanding treatment-subgroup effect in primary and secondary prevention of cardiovascular disease: An exploration using meta-analyses of individual patient data. J Clin Epidemiol 2021; 139:160-166. [PMID: 34400257 DOI: 10.1016/j.jclinepi.2021.08.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 08/05/2021] [Accepted: 08/10/2021] [Indexed: 11/17/2022]
Abstract
BACKGROUND AND OBJECTIVE Recommendations for preventing cardiovascular (CV) disease are currently separated into primary and secondary prevention. We hypothesize that relative effects of interventions for CV prevention are not different across primary and secondary prevention cohorts. Our aim was to test for differences in relative effects on CV events in common preventive CV interventions across primary and secondary prevention cohorts. METHODS AND RESULTS A systematic search was performed to identify individual patient data (IPD) meta-analyses that included both primary and secondary prevention populations. Eligibility assessment, data extraction, and risk of bias assessment were conducted independently and in duplicate. We extracted relative risks (RR) with 95% confidence intervals (95% CI) of the interventions over patient-important outcomes and estimated the ratio of RR for primary and secondary prevention populations. We identified five eligible IPDs representing 524,570 participants. Quality assessment resulted in overall low-to-moderate methodological quality. We found no subgroup effect across prevention categories in any of the outcomes assessed. CONCLUSION In the absence of significant treatment-subgroup interactions between primary and secondary CV prevention cohorts for common preventive interventions, clinical practice guidelines could offer recommendations tailored to individual estimates of CV risk without regard to membership to primary and secondary prevention cohorts. This would require the development of reliable ASCVD risk estimators that apply across both cohorts.
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Affiliation(s)
| | - Oscar J Ponce
- Knowledge and Evaluation Research Unit, Mayo Clinic, Rochester, MN, USA
| | - Meritxell Urtecho
- Knowledge and Evaluation Research Unit, Mayo Clinic, Rochester, MN, USA
| | - Gabriel F Torres
- School of Medicine, Cayetano Heredia Peruvian University, Lima, Peru
| | - Tereza Belluzzo
- Internal Medicine, Jablonec nad Nisou Hospital, Jablonec nad Nisou, Czech Republic
| | - Victor Montori
- Knowledge and Evaluation Research Unit, Mayo Clinic, Rochester, MN, USA
| | - Carolina Liu
- School of Medicine, Cayetano Heredia Peruvian University, Lima, Peru
| | - Francisco Barrera
- Knowledge and Evaluation Research Unit, Mayo Clinic, Rochester, MN, USA; Plataforma INVEST Medicina UANL-KER Unit Mayo Clinic (KER Unit Mexico), School of Medicine, Universidad Autonoma de Nuevo Leon, Monterrey, Nuevo Leon, Mexico
| | - Alejandro Diaz
- Plataforma INVEST Medicina UANL-KER Unit Mayo Clinic (KER Unit Mexico), School of Medicine, Universidad Autonoma de Nuevo Leon, Monterrey, Nuevo Leon, Mexico
| | - Larry Prokop
- Department of Library-Public Services, Mayo Clinic, Rochester, MN, USA
| | | | - Victor M Montori
- Knowledge and Evaluation Research Unit, Mayo Clinic, Rochester, MN, USA.
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39
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Impact of train/test sample regimen on performance estimate stability of machine learning in cardiovascular imaging. Sci Rep 2021; 11:14490. [PMID: 34262098 PMCID: PMC8280147 DOI: 10.1038/s41598-021-93651-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Accepted: 06/15/2021] [Indexed: 12/23/2022] Open
Abstract
As machine learning research in the field of cardiovascular imaging continues to grow, obtaining reliable model performance estimates is critical to develop reliable baselines and compare different algorithms. While the machine learning community has generally accepted methods such as k-fold stratified cross-validation (CV) to be more rigorous than single split validation, the standard research practice in medical fields is the use of single split validation techniques. This is especially concerning given the relatively small sample sizes of datasets used for cardiovascular imaging. We aim to examine how train-test split variation impacts the stability of machine learning (ML) model performance estimates in several validation techniques on two real-world cardiovascular imaging datasets: stratified split-sample validation (70/30 and 50/50 train-test splits), tenfold stratified CV, 10 × repeated tenfold stratified CV, bootstrapping (500 × repeated), and leave one out (LOO) validation. We demonstrate that split validation methods lead to the highest range in AUC and statistically significant differences in ROC curves, unlike the other aforementioned approaches. When building predictive models on relatively small data sets as is often the case in medical imaging, split-sample validation techniques can produce instability in performance estimates with variations in range over 0.15 in the AUC values, and thus any of the alternate validation methods are recommended.
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40
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Girerd N, Meune C, Duarte K, Vercamer V, Lopez-Sublet M, Mourad JJ. Evidence of a Blood Pressure Reduction During the COVID-19 Pandemic and Associated Lockdown Period: Insights from e-Health Data. Telemed J E Health 2021; 28:266-270. [PMID: 34101507 DOI: 10.1089/tmj.2021.0006] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
Background: Despite widespread investigation into the incidence of acute myocardial infarction during the coronavirus disease 2019 (COVID-19) pandemic and associated lockdown, no study has examined the situation's impact on blood pressure (BP) levels. Methods: Measurements of BP and heart rate (HR) were obtained from persons living in the Paris urban area using connected home BP monitors (accessible to patients and health providers through a secured server). Three time periods of e-health recordings were compared: during the pandemic before the lockdown, during the lockdown, and the same time period in 2019. Results: A total of 297,089 BP recordings from 2,273 participants (age 56.3 ± 12.8 years, 81.1% male) were made. During confinement, systolic BP gradually decreased by 3 mmHg (-2.4 to -3.9), and diastolic BP by 1.5 mmHg (-1.4 to -2.2) (all p < 0.001); this decrease was greater for participants with higher BP (p < 0.0001 each). No significant variation in HR was noted. Conclusion: Among a very large cohort, we observed a significant decrease in home BP measured with e-health devices during the first lockdown period. This study emphasizes the research potential of e-health during the COVID-19 crisis.
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Affiliation(s)
- Nicolas Girerd
- Lorraine University, School of Medicine, Clinical Investigation Center, Heart and Vessels Institute of Lorraine, Vandoeuvre les Nancy, France.,F-CRIN INI-CRCT (Cardiovascular and Renal Clinical Trialists), Nancy, France.,Cardiology Department, Heart and Vessels Institute of Lorraine, Nancy University Hospital, Vandoeuvre les Nancy, France
| | - Christophe Meune
- Department of Cardiology and Paris XIII University, Avicenne Hospital AP-HP, Bobigny, France
| | - Kevin Duarte
- National Institute of Health and Medical Research, Center for Clinical Multidisciplinary Research, University of Lorraine, Regional University Hospital of Nancy, French Clinical Research Infrastructure Network Investigation Network Initiative-Cardiovascular and Renal Clinical Trialists, Nancy, France
| | | | - Marilucy Lopez-Sublet
- Department of Internal Medicine and ESH Excellence Centre, Avicenne Hospital AP-HP, Bobigny, France
| | - Jean-Jacques Mourad
- Department of Internal Medicine and ESH Excellence Centre, Saint-Joseph Hospital, Paris, France
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Sanz-García A, Cecconi A, Vera A, Camarasaltas JM, Alfonso F, Ortega GJ, Jimenez-Borreguero J. Electrocardiographic biomarkers to predict atrial fibrillation in sinus rhythm electrocardiograms. Heart 2021; 107:1813-1819. [PMID: 34088763 DOI: 10.1136/heartjnl-2021-319120] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 04/19/2021] [Accepted: 04/23/2021] [Indexed: 11/03/2022] Open
Abstract
OBJECTIVE Early prediction of atrial fibrillation (AF) development would improve patient outcomes. We propose a simple and cheap ECG based score to predict AF development. METHODS A cohort of 16 316 patients was analysed. ECG measures provided by the computer-assisted ECG software were used to identify patients. A first group included patients in sinus rhythm who showed an ECG with AF at any time later (n=505). A second group included patients with all their ECGs in sinus rhythm (n=15 811). By using a training set (75% of the cohort) the initial sinus rhythm ECGs of both groups were analysed and a predictive risk score based on a multivariate logistic model was constructed. RESULTS A multivariate regression model was constructed with 32 variables showing a predictive value characterised by an area under the curve (AUC) of 0.776 (95% CI: 0.738 to 0.814). The subsequent risk score included the following variables: age, duration of P-wave in aVF, V4 and V5; duration of T-wave in V3, mean QT interval adjusted for heart rate, transverse P-wave clockwise rotation, transverse P-wave terminal angle and transverse QRS complex terminal vector magnitude. Risk score values ranged from 0 (no risk) to 5 (high risk). The predictive validity of the score reached an AUC of 0.764 (95% CI: 0.722 to 0.806) with a global specificity of 61% and a sensitivity of 55%. CONCLUSIONS The automatic assessment of ECG biomarkers from ECGs in sinus rhythm is able to predict the risk for AF providing a low-cost screening strategy for early detection of this pathology.
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Affiliation(s)
- Ancor Sanz-García
- Data Analysis Unit, Hospital Universitario de la Princesa, Madrid, Spain
| | - Alberto Cecconi
- Cardiology Department, Hospital Universitario de la Princesa, Madrid, Spain
| | - Alberto Vera
- Cardiology Department, Hospital Universitario de la Princesa, Madrid, Spain
| | | | - Fernando Alfonso
- Cardiology Department, Hospital Universitario de la Princesa, Madrid, Spain
| | - Guillermo Jose Ortega
- Data Analysis Unit, Hospital Universitario de la Princesa, Madrid, Spain .,CONICET; Consejo Nacional de Investigaciones Científicas y Técnicas, Buenos Aires, Argentina
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Xu D, Sheng JQ, Hu PJH, Huang TS, Hsu CC. A Deep Learning-Based Unsupervised Method to Impute Missing Values in Patient Records for Improved Management of Cardiovascular Patients. IEEE J Biomed Health Inform 2021; 25:2260-2272. [PMID: 33095720 DOI: 10.1109/jbhi.2020.3033323] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Physicians increasingly depend on electronic health records (EHRs) to manage their patients. However, many patient records have substantial missing values that pose a fundamental challenge to their clinical use. To address this prevailing challenge, we propose an unsupervised deep learning-based method that can facilitate physicians' use of EHRs to improve their management of cardiovascular patients. By building on the deep autoencoder framework, we develop a novel method to impute missing values in patient records. To demonstrate its clinical applicability and values, we use data from cardiovascular patients and evaluate the proposed method's imputation effectiveness and predictive efficacy, in comparison with six prevalent benchmark techniques. The proposed method can impute missing values and predict important patient outcomes more effectively than all the benchmark techniques. This study reinforces the importance of adequately addressing missing values in patient records. It further illustrates how effective imputations can enable greater predictive efficacy with regard to important patient outcomes, which are crucial to the use of EHRs and health analytics for improved patient management. Supported by the complete data imputed by the proposed method, physicians can make timely patient outcome estimations (predictions) and therapeutic treatment assessments.
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Goudot FX, Msadek S, Boukertouta T, Schischmanoff PO, Meune C. Routine use of natriuretic peptides: Lessons from a big data analysis. Ann Clin Biochem 2021; 58:481-486. [PMID: 34006120 DOI: 10.1177/00045632211020779] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Natriuretic peptides have broad indications during heart failure and the detection of left ventricular dysfunction in high-risk patients. They can also be used for the diagnosis/management of other cardiac diseases. However, very little is known regarding their use in routine practice. METHODS We examined all biological tests performed from February 2010 to August 2015 in two districts from the French Brittany, covering 13,653 km2 and including 22,265 physicians. We report the settings and conditions of N-terminal pro-B-type natriuretic peptide (NT-proBNP) measurements (the only locally natriuretic peptide available). RESULTS From a total of 3,606,432 tests requested in 557,650 adult (older than 20 years) patients, only 56,653 (1.6%) included at least one NT-proBNP measurement. NT-proBNP measurements gradually increased, from 9188 in 2011 to 12,938 in 2014 (P < 0.001). Most NT-proBNP tests were measured in urban laboratories (72.7%) and in private (62.9%) non-hospital/clinics laboratories; they were mostly ordered by general practitioners (66% compared with 11% by cardiologists). The number of NT-proBNP measurements increased with age up to 80-90 years, and 70.3% of tests were measured in ≥75 years patients. Creatinine and electrolytes were not associated with NT-proBNP in 15.8% and 19.7% of tests, respectively. CONCLUSION Among a very large cohort, we observed that natriuretic peptides remain largely undermeasured. NT-proBNP is mostly measured in elderly patients, and its interpretation may be hazardous in up to 16% of all individuals because no measurement of creatinine was associated to NT-proBNP.
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Affiliation(s)
- F X Goudot
- Cardiology Department, Avicenne University Hospital, APHP, Université Sorbonne Paris Nord, Bobigny, France
| | - S Msadek
- Cardiology Department, Avicenne University Hospital, APHP, Université Sorbonne Paris Nord, Bobigny, France
| | - T Boukertouta
- Cardiology Department, Avicenne University Hospital, APHP, Université Sorbonne Paris Nord, Bobigny, France
| | - P O Schischmanoff
- Biochemistry Department, Avicenne University Hospital, APHP, Université Sorbonne Paris Nord, Bobigny, France
| | - C Meune
- Cardiology Department, Avicenne University Hospital, APHP, Université Sorbonne Paris Nord, Bobigny, France
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Haq IU, Haq I, Xu B. Artificial intelligence in personalized cardiovascular medicine and cardiovascular imaging. Cardiovasc Diagn Ther 2021; 11:911-923. [PMID: 34295713 DOI: 10.21037/cdt.2020.03.09] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Accepted: 03/05/2020] [Indexed: 12/27/2022]
Abstract
The collection of large, heterogeneous electronic datasets and imaging from patients with cardiovascular disease (CVD) has lent itself to the use of sophisticated analysis using artificial intelligence (AI). AI techniques such as machine learning (ML) are able to identify relationships between data points by linking input to output variables using a combination of different functions, such as neural networks. In cardiovascular medicine, this is especially pertinent for classification, diagnosis, risk prediction and treatment guidance. Common cardiovascular data sources from patients include genomic data, cardiovascular imaging, wearable sensors and electronic health records (EHR). Leveraging AI in analysing such data points: (I) for clinicians: more accurate and streamlined image interpretation and diagnosis; (II) for health systems: improved workflow and reductions in medical errors; (III) for patients: promoting health with further education and promoting primary and secondary cardiovascular health prevention. This review addresses the need for AI in cardiovascular medicine by reviewing recent literature in different cardiovascular imaging modalities: electrocardiography, echocardiography, cardiac computed tomography, cardiac nuclear imaging, and cardiac magnetic resonance (CMR) imaging as well as the role of EHR. This review aims to conceptulise these studies in relation to their clinical applications, potential limitations and future opportunities and directions.
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Affiliation(s)
- Ikram-Ul Haq
- Imperial College London Faculty of Medicine, London, UK
| | - Iqraa Haq
- Imperial College London Faculty of Medicine, London, UK
| | - Bo Xu
- Section of Cardiovascular Imaging, Robert and Suzanne Tomsich Department of Cardiovascular Medicine, Sydell and Arnold Family Heart, Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, OH, USA
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Allareddy V, Lee MK, Vaid NR, Yadav S. Use of Neural Network model to examine post-operative infections following orthognathic surgeries in the United States. Semin Orthod 2021. [DOI: 10.1053/j.sodo.2021.05.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Gajardo AIJ, Henríquez F, Llancaqueo M. Big data, social determinants of coronary heart disease and barriers for data access. Eur J Prev Cardiol 2021; 28:397-399. [PMID: 32363915 DOI: 10.1177/2047487320922366] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Affiliation(s)
- Abraham I J Gajardo
- Department of Medicine, Hospital Clínico Universidad de Chile, Chile
- Laboratory of Oxidative Stress, Institute of Biomedical Sciences, Faculty of Medicine, Universidad de Chile, Chile
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Jadidi M, Poulson W, Aylward P, MacTaggart J, Sanderfer C, Marmie B, Pipinos M, Kamenskiy A. Calcification prevalence in different vascular zones and its association with demographics, risk factors, and morphometry. Am J Physiol Heart Circ Physiol 2021; 320:H2313-H2323. [PMID: 33961507 DOI: 10.1152/ajpheart.00040.2021] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Vascular calcification is associated with a higher incidence of cardiovascular events, but its prevalence in different vascular zones and the influence of demographics, risk factors, and morphometry remain insufficiently understood. Computerized tomography angiography scans from 211 subjects 5-93 yr old (mean age 47 ± 24 yr, 127 M/84 F) were used to build 3D vascular reconstructions and measure arterial diameters, tortuosity, and calcification volumes in six vascular zones spanning from the ascending thoracic aorta to the pelvic arteries. A machine learning random forest algorithm was used to determine the associations between calcification in each zone with demographics, risk factors, and vascular morphometry. Calcification appeared during the fourth decade of life and was present in all subjects after 65 yr. The abdominal aorta and the iliofemoral segment were the first to develop calcification, whereas the ascending thoracic aorta was the last. Demographics and risk factors explained 33-59% of the variation in calcification. Age, creatinine level, body mass index, coronary artery disease, and hypertension were the strongest contributors, whereas the effects of sex, race, tobacco use, diabetes, dyslipidemia, and alcohol and substance use disorders on calcification were small. Vascular morphometry did not directly and independently affect calcium burden. Vascular zones develop calcification asynchronously, with distal segments calcifying first. Understanding the influence of demographics and risk factors on calcium prevalence can help better understand the disease pathophysiology and may help with the early identification of patients that are at higher risk of cardiovascular events.NEW & NOTEWORTHY We investigated the prevalence of vascular calcification in different zones of the aorta and pelvic arteries using computerized tomography angiography reconstructions and have applied machine learning to determine how calcification is affected by demographics, risk factors, and morphometry. The presented data can help identify patients at higher risk of developing vascular calcification that may lead to cardiovascular events.
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Affiliation(s)
- Majid Jadidi
- Department of Biomechanics, University of Nebraska at Omaha, Omaha, Nebraska
| | - William Poulson
- Department of Surgery, University of Nebraska Medical Center, Omaha, Nebraska
| | - Paul Aylward
- Department of Surgery, University of Nebraska Medical Center, Omaha, Nebraska
| | - Jason MacTaggart
- Department of Surgery, University of Nebraska Medical Center, Omaha, Nebraska
| | - Christian Sanderfer
- Department of Surgery, University of Nebraska Medical Center, Omaha, Nebraska
| | - Blake Marmie
- Department of Surgery, University of Nebraska Medical Center, Omaha, Nebraska
| | - Margarita Pipinos
- Department of Surgery, University of Nebraska Medical Center, Omaha, Nebraska
| | - Alexey Kamenskiy
- Department of Biomechanics, University of Nebraska at Omaha, Omaha, Nebraska
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Lv H, Yang X, Wang B, Wang S, Du X, Tan Q, Hao Z, Liu Y, Yan J, Xia Y. Machine Learning-Driven Models to Predict Prognostic Outcomes in Patients Hospitalized With Heart Failure Using Electronic Health Records: Retrospective Study. J Med Internet Res 2021; 23:e24996. [PMID: 33871375 PMCID: PMC8094022 DOI: 10.2196/24996] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 01/04/2021] [Accepted: 03/16/2021] [Indexed: 01/16/2023] Open
Abstract
Background With the prevalence of cardiovascular diseases increasing worldwide, early prediction and accurate assessment of heart failure (HF) risk are crucial to meet the clinical demand. Objective Our study objective was to develop machine learning (ML) models based on real-world electronic health records to predict 1-year in-hospital mortality, use of positive inotropic agents, and 1-year all-cause readmission rate. Methods For this single-center study, we recruited patients with newly diagnosed HF hospitalized between December 2010 and August 2018 at the First Affiliated Hospital of Dalian Medical University (Liaoning Province, China). The models were constructed for a population set (90:10 split of data set into training and test sets) using 79 variables during the first hospitalization. Logistic regression, support vector machine, artificial neural network, random forest, and extreme gradient boosting models were investigated for outcome predictions. Results Of the 13,602 patients with HF enrolled in the study, 537 (3.95%) died within 1 year and 2779 patients (20.43%) had a history of use of positive inotropic agents. ML algorithms improved the performance of predictive models for 1-year in-hospital mortality (areas under the curve [AUCs] 0.92-1.00), use of positive inotropic medication (AUCs 0.85-0.96), and 1-year readmission rates (AUCs 0.63-0.96). A decision tree of mortality risk was created and stratified by single variables at levels of high-sensitivity cardiac troponin I (<0.068 μg/L), followed by percentage of lymphocytes (<14.688%) and neutrophil count (4.870×109/L). Conclusions ML techniques based on a large scale of clinical variables can improve outcome predictions for patients with HF. The mortality decision tree may contribute to guiding better clinical risk assessment and decision making.
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Affiliation(s)
- Haichen Lv
- Department of Cardiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Xiaolei Yang
- Department of Cardiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Bingyi Wang
- Medical Department, Yidu Cloud (Beijing) Technology Co Ltd, Beijing, China
| | - Shaobo Wang
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China.,AI Lab, Yidu Cloud (Beijing) Technology Co Ltd, Beijing, China
| | - Xiaoyan Du
- Medical Department, Yidu Cloud (Beijing) Technology Co Ltd, Beijing, China
| | - Qian Tan
- Medical Department, Happy Life Technology Co Ltd, Beijing, China
| | - Zhujing Hao
- Department of Cardiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Ying Liu
- Department of Cardiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Jun Yan
- AI Lab, Yidu Cloud (Beijing) Technology Co Ltd, Beijing, China
| | - Yunlong Xia
- Department of Cardiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
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Borges do Nascimento IJ, Marcolino MS, Abdulazeem HM, Weerasekara I, Azzopardi-Muscat N, Gonçalves MA, Novillo-Ortiz D. Impact of Big Data Analytics on People's Health: Overview of Systematic Reviews and Recommendations for Future Studies. J Med Internet Res 2021; 23:e27275. [PMID: 33847586 PMCID: PMC8080139 DOI: 10.2196/27275] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 02/19/2021] [Accepted: 03/24/2021] [Indexed: 12/17/2022] Open
Abstract
Background Although the potential of big data analytics for health care is well recognized, evidence is lacking on its effects on public health. Objective The aim of this study was to assess the impact of the use of big data analytics on people’s health based on the health indicators and core priorities in the World Health Organization (WHO) General Programme of Work 2019/2023 and the European Programme of Work (EPW), approved and adopted by its Member States, in addition to SARS-CoV-2–related studies. Furthermore, we sought to identify the most relevant challenges and opportunities of these tools with respect to people’s health. Methods Six databases (MEDLINE, Embase, Cochrane Database of Systematic Reviews via Cochrane Library, Web of Science, Scopus, and Epistemonikos) were searched from the inception date to September 21, 2020. Systematic reviews assessing the effects of big data analytics on health indicators were included. Two authors independently performed screening, selection, data extraction, and quality assessment using the AMSTAR-2 (A Measurement Tool to Assess Systematic Reviews 2) checklist. Results The literature search initially yielded 185 records, 35 of which met the inclusion criteria, involving more than 5,000,000 patients. Most of the included studies used patient data collected from electronic health records, hospital information systems, private patient databases, and imaging datasets, and involved the use of big data analytics for noncommunicable diseases. “Probability of dying from any of cardiovascular, cancer, diabetes or chronic renal disease” and “suicide mortality rate” were the most commonly assessed health indicators and core priorities within the WHO General Programme of Work 2019/2023 and the EPW 2020/2025. Big data analytics have shown moderate to high accuracy for the diagnosis and prediction of complications of diabetes mellitus as well as for the diagnosis and classification of mental disorders; prediction of suicide attempts and behaviors; and the diagnosis, treatment, and prediction of important clinical outcomes of several chronic diseases. Confidence in the results was rated as “critically low” for 25 reviews, as “low” for 7 reviews, and as “moderate” for 3 reviews. The most frequently identified challenges were establishment of a well-designed and structured data source, and a secure, transparent, and standardized database for patient data. Conclusions Although the overall quality of included studies was limited, big data analytics has shown moderate to high accuracy for the diagnosis of certain diseases, improvement in managing chronic diseases, and support for prompt and real-time analyses of large sets of varied input data to diagnose and predict disease outcomes. Trial Registration International Prospective Register of Systematic Reviews (PROSPERO) CRD42020214048; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=214048
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Affiliation(s)
- Israel Júnior Borges do Nascimento
- School of Medicine, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil.,Department of Medicine, School of Medicine, Medical College of Wisconsin, Wauwatosa, WI, United States
| | - Milena Soriano Marcolino
- Department of Internal Medicine, University Hospital, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil.,School of Medicine and Telehealth Center, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | | | - Ishanka Weerasekara
- School of Health Sciences, Faculty of Health and Medicine, The University of Newcastle, Callaghan, Australia.,Department of Physiotherapy, Faculty of Allied Health Sciences, University of Peradeniya, Peradeniya, Sri Lanka
| | - Natasha Azzopardi-Muscat
- Division of Country Health Policies and Systems, World Health Organization, Regional Office for Europe, Copenhagen, Denmark
| | - Marcos André Gonçalves
- Department of Computer Science, Institute of Exact Sciences, Federal University of Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - David Novillo-Ortiz
- Division of Country Health Policies and Systems, World Health Organization, Regional Office for Europe, Copenhagen, Denmark
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Bakker L, Aarts J, Uyl-de Groot C, Redekop W. Economic evaluations of big data analytics for clinical decision-making: a scoping review. J Am Med Inform Assoc 2021; 27:1466-1475. [PMID: 32642750 PMCID: PMC7526472 DOI: 10.1093/jamia/ocaa102] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Revised: 04/06/2020] [Accepted: 05/11/2020] [Indexed: 12/17/2022] Open
Abstract
OBJECTIVE Much has been invested in big data analytics to improve health and reduce costs. However, it is unknown whether these investments have achieved the desired goals. We performed a scoping review to determine the health and economic impact of big data analytics for clinical decision-making. MATERIALS AND METHODS We searched Medline, Embase, Web of Science and the National Health Services Economic Evaluations Database for relevant articles. We included peer-reviewed papers that report the health economic impact of analytics that assist clinical decision-making. We extracted the economic methods and estimated impact and also assessed the quality of the methods used. In addition, we estimated how many studies assessed "big data analytics" based on a broad definition of this term. RESULTS The search yielded 12 133 papers but only 71 studies fulfilled all eligibility criteria. Only a few papers were full economic evaluations; many were performed during development. Papers frequently reported savings for healthcare payers but only 20% also included costs of analytics. Twenty studies examined "big data analytics" and only 7 reported both cost-savings and better outcomes. DISCUSSION The promised potential of big data is not yet reflected in the literature, partly since only a few full and properly performed economic evaluations have been published. This and the lack of a clear definition of "big data" limit policy makers and healthcare professionals from determining which big data initiatives are worth implementing.
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Affiliation(s)
- Lytske Bakker
- Erasmus School of Health Policy and Management, Erasmus University, Rotterdam, Netherlands.,Institute for Medical Technology Assessment, Erasmus University, Rotterdam, Netherlands
| | - Jos Aarts
- Erasmus School of Health Policy and Management, Erasmus University, Rotterdam, Netherlands
| | - Carin Uyl-de Groot
- Erasmus School of Health Policy and Management, Erasmus University, Rotterdam, Netherlands.,Institute for Medical Technology Assessment, Erasmus University, Rotterdam, Netherlands
| | - William Redekop
- Erasmus School of Health Policy and Management, Erasmus University, Rotterdam, Netherlands.,Institute for Medical Technology Assessment, Erasmus University, Rotterdam, Netherlands
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