用日常病历数据提前一年预警多种慢性病，还让模型决策可解释。
An Explainable Disease Surveillance System for Early Prediction of Multiple Chronic Diseases
- 基于病史、体征等常规数据，用随机森林预测3/6/12个月慢性病风险。
- 模型在专家评估中表现良好，F1和AUROC均达临床可用水平。
- 新增规则引擎提升模型可解释性，适合临床医生用于早期干预。
本研究针对医疗体系中的关键空白，构建了一个临床有意义、实用且可解释的多慢性病监测系统，利用整合自多家美国医疗机构的日常电子健康记录（EHR）数据，通过CureMD的EMR/EHR系统实现。不同于依赖实验室指标的传统方法，本方案聚焦于可常规获取的医疗历史、生命体征、诊断与用药信息，用于预判未来一年内慢性病发病风险。针对每种慢性病训练了三种模型，分别预测3、6、12个月后的患病风险。采用随机森林模型，并以F1分数与AUROC作为内部验证指标；同时由专家医生团队依据医学知识对模型推断进行临床相关性评估。此外，探讨了模型在实际EMR系统中的集成方式。除使用Shapley值与代理模型增强可解释性外，还提出一种新型规则工程框架，提升随机森林的内在可解释性。
原文摘要 · Abstract (English)
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.
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