Using AI models to improve prediction of CKD's progression to end-stage renal disease

Yubo Li, coauthor, PhD student at Carnegie Mellon's Heinz CollegeOur work bridges a critical gap by developing a framework that uses integrated clinical and claims data rather than isolated data sources. 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.

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Carnegie Mellon University

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