An Explainable Disease Surveillance System for Early Prediction of Multiple Chronic Diseases

TL;DR

This paper presents an explainable, practical disease surveillance system that predicts multiple chronic diseases using routine EHR data, with models validated for clinical relevance and integrated into EMR systems.

Contribution

The study introduces a novel, explainable prediction framework for multiple chronic diseases using routine data, and demonstrates its integration into clinical EMR systems.

Findings

Models achieved high F1 scores and AUROC metrics.

Expert physicians validated the clinical relevance of the models.

The system enhances early detection and interpretability of chronic disease risks.

Abstract

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.