CMU Research Uses AI to Better Predict Kidney Failure
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Carnegie Mellon University researchers have created new AI models that do a better job of predicting which patients with chronic kidney disease (CKD) will go on to develop end-stage renal disease (ESRD). By combining medical records and insurance data, their approach gives doctors a better chance of catching the disease earlier, the ability to plan care more effectively and help reduce health gaps for people living with kidney disease.
“Our study presents a robust framework for predicting ESRD outcomes, improving clinical decision-making through integrated multisourced data and advanced analytics,” explained Rema Padman(opens in new window), Trustees Professor of Management Science and Healthcare Informatics at Carnegie Mellon’s Heinz College of Information Systems and Public Policy(opens in new window), who led the study(opens in new window). “Future research will expand data integration and extend this framework to other chronic diseases.”
CKD is a long-term condition where kidney function slowly declines, sometimes leading to ESRD, when kidneys lose nearly all function and patients need dialysis or a transplant to survive. Globally, CKD affects between 8% and 16% of people, with about 5% to 10% eventually developing ESRD.
The disease is expensive: in the U.S., Medicare spends a disproportionate amount on CKD patients, especially those who reach ESRD. Many ESRD patients are also readmitted to the hospital within a month of discharge, underscoring the urgent need for earlier detection and better management.
For this study, the researchers looked at data from more than 10,000 CKD patients collected between 2009 and 2018. They tested several statistical, machine learning and deep learning models, using different time windows to see how early predictions could be made accurately. The models that combined both clinical and claims data performed better than those using only one type of information.
They found that a 24-month observation window offered the best balance between early detection and prediction accuracy.
“Our work bridges a critical gap by developing a framework that uses integrated clinical and claims data rather than isolated data sources,” noted Yubo Li, a Ph.D. student at Heinz who co-authored the study. “By minimizing the observation window needed for accurate predictions, our approach balances clinical relevance with patient-centered practicality; this integration enhances both predictive accuracy and clinical utility, enabling more informed decision-making to improve patient outcomes.”
Among the study’s limitations, the authors say their reliance on data from one institution may limit the generalizability of their model to other care settings. In addition, their use of data from electronic health records can introduce observational bias, incomplete records and underrepresentation of certain patient groups, which can undermine both accuracy and fairness.
The research was funded by the Center for Machine Learning and Health(opens in new window) at Carnegie Mellon University.
