Towards Interpretable End-Stage Renal Disease (ESRD) Prediction: Utilizing Administrative Claims Data with Explainable AI Techniques

Yubo Li, Saba Al-Sayouri, Rema Padman
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Abstract

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.
实现可解释的终末期肾病 (ESRD) 预测:利用行政索赔数据和可解释的人工智能技术
本研究探讨了利用行政报销数据,结合先进的机器学习和深度学习技术,预测慢性肾脏病(CKD)向终末期肾病(ESRD)进展的潜力。我们分析了一家大型医疗保险机构提供的为期 10 年的综合数据集,利用随机森林(RandomForest)和 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,开发了多个观察窗的预测模型。我们的研究结果表明,LSTM 模型,尤其是在 24 个月的观察窗口中,在预测 ESRD 进展方面表现出卓越的性能,优于文献中的现有模型。我们还进一步应用了SHAPLEY Additive exPlanations(SHAP)分析来增强可解释性,从而深入了解个体特征对患者个体水平预测的影响。这项研究强调了利用行政报销数据进行 CKD 管理和预测 ESRD 进展的价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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