Key result
Machine learning and deep learning models applied to diverse electronic health record data demonstrated expert-comparable accuracy in predicting heart failure diagnosis, hospital readmission, and mortality.
Why the study?
Heart failure diagnosis, readmission, and mortality prediction are essential for developing personalized prevention and treatment plans, given high disease incidence and mortality.
Do machine learning models using EHR data improve heart failure diagnosis, readmission, and mortality prediction?
Do machine learning models using EHR data improve heart failure diagnosis, readmission, and mortality prediction?
Machine learning models utilizing EHR data show promise in accurately predicting heart failure diagnosis, readmission, and mortality, potentially facilitating personalized care.
ML models show potential for HF prediction from EHR data; leaves open need for prospective validation before clinical use.
Purpose of Review One in five people will develop heart failure (HF), and 50% of HF patients die in 5 years. The HF diagnosis, readmission, and mortality prediction are essential to develop personalized prevention and treatment plans. This review summarizes recent findings and approaches of machine learning models for HF diagnostic and outcome prediction using electronic health record (EHR) data. Recent Findings A set of machine learning models have been developed for HF diagnostic and outcome prediction using diverse variables derived from EHR data, including demographic, medical note, laboratory, and image data, and achieved expert-comparable prediction results. Summary Machine learning models can facilitate the identification of HF patients, as well as accurate patient-specific assessment of their risk for readmission and mortality. Additionally, novel machine learning techniques for integration of diverse data and improvement of model predictive accuracy in imbalanced data sets are critical for further development of these promising modeling methodologies.
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Guo et al. (2020) conducted a review in Heart Failure. Machine learning and deep learning models was evaluated on Prediction accuracy for heart failure diagnosis, readmission, and mortality. Machine learning and deep learning models applied to diverse electronic health record data demonstrated expert-comparable accuracy in predicting heart failure diagnosis, hospital readmission, and mortality.
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