Decision support systems (DSS) for predicting hypertensive events using real-world telemonitoring data.

OBJECTIVE: This study contributes to the limited literature on applying machine learning (ML) to telemonitoring data for timely decision support systems (DSS). We propose an end-to-end DSS framework to enable clinicians to intervene proactively by generating actionable information from daily telemonitoring data. The goal was to predict the probability of patients having an adverse event in the next 7 days using real-world data. We focus on the framework for effective development of a human-computer hybrid system that utilizes ML models to predict adverse hypertensive events and evaluate different ML models to determine optimal settings. Our goal is to create a system that enhances collaboration between ML and clinical expertise in hypertension management. MATERIALS AND METHODS: ML models (i.e., Logistic Regression, Random Forest (RF), XGBoost, Fusion Neural Network) were trained and evaluated with tenfold cross-validation, using AUCPR, AUCROC, and F1 score to study optimal configurations. In addition, different methods for calculating daily risk scores were compared. Finally, SHapley Additive exPlanations (SHAP) was used to investigate feature importance. RESULTS AND DISCUSSION: A total of 345,072 sliding windows from 2766 patients were used to study key questions for predicting hypertensive events. First, all ML models had comparable results at AUCPR ≃ 75% and AUCROC ≃ 87% with XGBoost having slightly better results. The optimal period of prediction to use as an input was 10 days, regardless of the ML models. Second, averaging the results across models gave the best performance for a single daily composite risk score. Finally, the key features observed in XGBoost and RF were daily Systolic Blood Pressure (SBP) readings, SBP variance, and Diastolic BP variance. However, key features may differ across different ML models. CONCLUSION: We demonstrate the need to report rigorous transparent details and evaluations to develop successful systems that can provide proactive alerts to manage adverse events.