将生存分析与分类结合，提升慢性病早期风险预测精度。
Survival Meets Classification: A Novel Framework for Early Risk Prediction Models of Chronic Diseases
- 融合生存分析与分类技术，构建统一预测框架。
- 在真实医疗数据上表现优于或媲美LightGBM、XGBoost等模型。
- 生成可临床验证的解释，适合医疗领域应用。
慢性病需长期医疗管理。基于大规模电子病历数据，我们为糖尿病、高血压、慢性肾病（CKD）、慢阻肺（COPD）和慢性缺血性心脏病五类常见慢性病开发了早期风险预测模型。本文提出一种新方法，将生存分析与分类技术融合。传统模型通常仅使用生存分析或分类独立建模。本研究证明，通过重构生存分析方法，可高效实现分类任务，使其成为疾病风险监测的综合工具。在真实世界大数据上的实验表明，所提生存模型在准确率、F1分数和AUROC指标上达到或超过现有最优模型（如LightGBM、XGBoost）。此外，该模型采用新方法生成可解释性结果，经三位临床专家验证，具备临床可用性。
原文摘要 · Abstract (English)
Chronic diseases are long-lasting conditions that require lifelong medical attention. Using big EMR data, we have developed early disease risk prediction models for five common chronic diseases: diabetes, hypertension, CKD, COPD, and chronic ischemic heart disease. In this study, we present a novel approach for disease risk models by integrating survival analysis with classification techniques. Traditional models for predicting the risk of chronic diseases predominantly focus on either survival analysis or classification independently. In this paper, we show survival analysis methods can be re-engineered to enable them to do classification efficiently and effectively, thereby making them a comprehensive tool for developing disease risk surveillance models. The results of our experiments on real-world big EMR data show that the performance of survival models in terms of accuracy, F1 score, and AUROC is comparable to or better than that of prior state-of-the-art models like LightGBM and XGBoost. Lastly, the proposed survival models use a novel methodology to generate explanations, which have been clinically validated by a panel of three expert physicians.
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