用医保数据+AI预测肾病进展，模型可解释性更强。
Towards Interpretable End-Stage Renal Disease (ESRD) Prediction: Utilizing Administrative Claims Data with Explainable AI Techniques
- 用10年医保数据训练LSTM模型，24个月观察窗口效果最佳。
- LSTM在预测终末期肾病上优于现有模型，性能更优。
- 结合SHAP分析，能看清每个患者的关键影响因素。
本研究探索利用医保数据与先进机器学习及深度学习技术，预测慢性肾病（CKD）向终末期肾病（ESRD）进展的可能性。基于某大型保险公司提供的十年完整数据集，我们采用随机森林、XGBoost等传统机器学习方法以及长短期记忆（LSTM）网络等深度学习方法，在多个观察窗口下构建预测模型。结果表明，采用24个月观察窗口的LSTM模型在预测ESRD进展方面表现最优，优于文献中已有模型。此外，我们应用SHapley Additive exPlanations（SHAP）分析提升模型可解释性，实现对个体患者层面特征贡献的可视化分析。研究证明，利用医保数据结合可解释人工智能技术，对慢性肾病管理与疾病进展预测具有重要价值。
原文摘要 · Abstract (English)
This study explores the potential of utilizing administrative claims data, combined with advanced machine learning and deep learning techniques, to predict the progression of Chronic Kidney Disease (CKD) to End-Stage Renal Disease (ESRD). We analyze a comprehensive, 10-year dataset provided by a major health insurance organization to develop prediction models for multiple observation windows using traditional machine learning methods such as Random Forest and XGBoost as well as deep learning approaches such as Long Short-Term Memory (LSTM) networks. Our findings demonstrate that the LSTM model, particularly with a 24-month observation window, exhibits superior performance in predicting ESRD progression, outperforming existing models in the literature. We further apply SHapley Additive exPlanations (SHAP) analysis to enhance interpretability, providing insights into the impact of individual features on predictions at the individual patient level. This study underscores the value of leveraging administrative claims data for CKD management and predicting ESRD progression.
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