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Heart failure (HF) is a major cause of mortality. Accurately monitoring HFprogress and adjust therapies are critical for improving patient outcomes. Anexperienced cardiologist can make accurate HF stage diagnoses based oncombination of symptoms, signs, and lab results from the electronic healthrecords (EHR) of a patient, without directly measuring heart function. Weexamined whether machine learning models, more specifically the XGBoost model,can accurately predict patient stage based on EHR, and we further applied theSHapley Additive exPlanations (SHAP) framework to identify informative featuresand their interpretations. Our results indicate that based on structured datafrom EHR, our models could predict patients' ejection fraction (EF) scores withmoderate accuracy. SHAP analyses identified informative features and revealedpotential clinical subtypes of HF. Our findings provide insights on how todesign computing systems to accurately monitor disease progression of HFpatients through continuously mining patients' EHR data.

Understanding Heart-Failure Patients EHR Clinical Features via SHAP Interpretation of Tree-Based Machine Learning Model Predictions