This chapter aims to provide a comprehensive overview of how ML and DL can be applied to EHRs for early disease detection and prevention, and highlights methodological advances, practical applications, and two real-world case studies, while addressing the challenges that must be overcome for safe and trustworthy clinic...
Zhi-Qiang Huo, Jian-Hua Wu· Methods in molecular biology· 0 citations
FIND-HF is a scalable EHR-based model which has the potential to help rule out undiagnosed HF in low risk cases, whilst high risk cases are associated with more advanced cardiac dysfunction and worse prognosis.
Y. Nakao, R. Nadarajah, F. Shuweihdi et al.· Scientific Reports· 0 citations
A supervised machine learning algorithm for AF in a Western Pacific population was derived and demonstrated that higher risk was associated with hospitalisation for other cardio-renal diseases and death.
J. Hsu, C. Hayward, Tobin Joseph et al.· Heart, Lung and Circulation· 0 citations
The EHR-based machine learning model, FIND-AF 2.0, identifies a high-risk subpopulation for AF diagnosis among patients at elevated risk of stroke and could enable scalable, EHR-driven, risk-guided AF screening.
R. Nadarajah, Jianhua Wu, A. Wahab et al.· Circulation· 0 citations
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