Biases in Electronic Health Records Data for Generating Real-World Evidence: An Overview

Al-Sahab, Ban; Leviton, Alan; Loddenkemper, Tobias; Paneth, Nigel; Zhang, Bo

doi:10.1007/s41666-023-00153-2

J Healthc Inform Res

2023

DOI: 10.1007/s41666-023-00153-2

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Biases in Electronic Health Records Data for Generating Real-World Evidence: An Overview

Ban Al-Sahab,

Alan Leviton,

Tobias Loddenkemper

et al.

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Cited by 4 publications

References 110 publications

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Cardiovascular care with digital twin technology in the era of generative artificial intelligence

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Digital twins, which are in silico replications of an individual and its environment, have advanced clinical decision-making and prognostication in cardiovascular medicine. The technology enables personalized simulations of clinical scenarios, prediction of disease risk, and strategies for clinical trial augmentation. Current applications of cardiovascular digital twins have integrated multi-modal data into mechanistic and statistical models to build physiologically accurate cardiac replicas to enhance disease phenotyping, enrich diagnostic workflows, and optimize procedural planning. Digital twin technology is rapidly evolving in the setting of newly available data modalities and advances in generative artificial intelligence, enabling dynamic and comprehensive simulations unique to an individual. These twins fuse physiologic, environmental, and healthcare data into machine learning and generative models to build real-time patient predictions that can model interactions with the clinical environment to accelerate personalized patient care. This review summarizes digital twins in cardiovascular medicine and their potential future applications by incorporating new personalized data modalities. It examines the technical advances in deep learning and generative artificial intelligence that broaden the scope and predictive power of digital twins. Finally, it highlights the individual and societal challenges as well as ethical considerations that are essential to realizing the future vision of incorporating cardiology digital twins into personalized cardiovascular care.

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Biases in Electronic Health Records Data for Generating Real-World Evidence: An Overview

Cited by 4 publications

References 110 publications

Electronic Health Records-based identification of newly diagnosed Crohn’s Disease cases

Electronic Health Records-based identification of newly diagnosed Crohn’s Disease cases

Training size predictably improves machine learning-based epileptic seizure forecasting from wearables

Cardiovascular care with digital twin technology in the era of generative artificial intelligence

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