{"title":"Towards Interpretable End-Stage Renal Disease (ESRD) Prediction: Utilizing Administrative Claims Data with Explainable AI Techniques","authors":"Yubo Li, Saba Al-Sayouri, Rema Padman","doi":"arxiv-2409.12087","DOIUrl":null,"url":null,"abstract":"This study explores the potential of utilizing administrative claims data,\ncombined with advanced machine learning and deep learning techniques, to\npredict the progression of Chronic Kidney Disease (CKD) to End-Stage Renal\nDisease (ESRD). We analyze a comprehensive, 10-year dataset provided by a major\nhealth insurance organization to develop prediction models for multiple\nobservation windows using traditional machine learning methods such as Random\nForest and XGBoost as well as deep learning approaches such as Long Short-Term\nMemory (LSTM) networks. Our findings demonstrate that the LSTM model,\nparticularly with a 24-month observation window, exhibits superior performance\nin predicting ESRD progression, outperforming existing models in the\nliterature. We further apply SHapley Additive exPlanations (SHAP) analysis to\nenhance interpretability, providing insights into the impact of individual\nfeatures on predictions at the individual patient level. This study underscores\nthe value of leveraging administrative claims data for CKD management and\npredicting ESRD progression.","PeriodicalId":501301,"journal":{"name":"arXiv - CS - Machine Learning","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Machine Learning","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.12087","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
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.