An Explainable Disease Surveillance System for Early Prediction of Multiple Chronic Diseases
It addresses a critical gap in healthcare by providing early, explainable predictions for chronic diseases using routinely available data, though it is incremental in method.
This study developed an explainable disease surveillance system using routine EHR data to predict multiple chronic diseases up to 12 months before diagnosis, achieving performance validated with F1 scores and AUROC metrics and clinical relevance assessed by expert physicians.
This study addresses a critical gap in the healthcare system by developing a clinically meaningful, practical, and explainable disease surveillance system for multiple chronic diseases, utilizing routine EHR data from multiple U.S. practices integrated with CureMD's EMR/EHR system. Unlike traditional systems--using AI models that rely on features from patients' labs--our approach focuses on routinely available data, such as medical history, vitals, diagnoses, and medications, to preemptively assess the risks of chronic diseases in the next year. We trained three distinct models for each chronic disease: prediction models that forecast the risk of a disease 3, 6, and 12 months before a potential diagnosis. We developed Random Forest models, which were internally validated using F1 scores and AUROC as performance metrics and further evaluated by a panel of expert physicians for clinical relevance based on inferences grounded in medical knowledge. Additionally, we discuss our implementation of integrating these models into a practical EMR system. Beyond using Shapley attributes and surrogate models for explainability, we also introduce a new rule-engineering framework to enhance the intrinsic explainability of Random Forests.