  Heart failure (HF) is a major cause of mortality. Accurately monitoring HF
progress and adjust therapies are critical for improving patient outcomes. An
experienced cardiologist can make accurate HF stage diagnoses based on
combination of symptoms, signs, and lab results from the electronic health
records (EHR) of a patient, without directly measuring heart function. We
examined whether machine learning models, more specifically the XGBoost model,
can accurately predict patient stage based on EHR, and we further applied the
SHapley Additive exPlanations (SHAP) framework to identify informative features
and their interpretations. Our results indicate that based on structured data
from EHR, our models could predict patients' ejection fraction (EF) scores with
moderate accuracy. SHAP analyses identified informative features and revealed
potential clinical subtypes of HF. Our findings provide insights on how to
design computing systems to accurately monitor disease progression of HF
patients through continuously mining patients' EHR data.
