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

Presentation Time: 09:45 AM - 10:00 AM

Abstract Keywords: Clinical Decision Support, Deep Learning, Machine Learning
Working Group: Clinical Decision Support Working Group
Primary Track: Applications
Programmatic Theme: Clinical Informatics

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 LSTM models, particularly with a 24-month observation window, exhibit 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 individual impact of 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.

Speaker(s):
Yubo Li, Information Systems Ph.D.
Carnegie Mellon University

Author(s):
Rema Padman, PhD - Carnegie Mellon University; Saba Al-Sayouri, PhD - National Institutes of Health;