Machine learning enhanced expert system for detecting heart failure decompensation using patient reported vitals and electronic health records

Heart failure (HF) is a condition with periods of stability interrupted by periods of worsening symptoms, known as decompensation episodes. Digital interventions are promising tools to alleviate burdens on HF management through automated alerts at the earliest decompensation sign. To accomplish this, our lab developed Medly, an expert system-enhanced digital therapeutic program for HF patients. Medly's algorithm is a knowledge-based system that analyzes weight, blood pressure, and heart rate and sends automated alerts to clinicians and patients if deterioration is identified. Rules were set conservatively to account for false negatives. However, reducing false negatives resulted in an increase in false positives, which can lead to unnecessary clinical workload. Further, patients' electronic health records (EHR) were not used when developing the rules-based algorithm. This study aimed to enhance Medly's performance with machine learning and include a richer set of data, including EHR, for predicting decompensated HF episodes. We performed a retrospective study using XGBoost for the binary classification of whether the patient needed to be contacted for a possible decompensation episode. Features included blood pressure, weight change, heart rate, and EHR data (e.g., blood work, medication history). We further performed interpretability analysis to investigate the importance of including EHR data in the model. The enhanced algorithm achieved 98.08% accuracy, 95.26% sensitivity, 98.86% specificity, and a PPV of 88.18% - a marked improvement over the 55.8% in the rules-based algorithm. EHR data, mainly B-type natriuretic peptide (BNP) and total cholesterol, was crucial in predicting decompensation and correcting false-positive alerting.