Early prediction of end-stage kidney disease using electronic health record data: a machine learning approach with a 2-year horizon.

ABSTRACT:

Objectives

In the United States, end-stage kidney disease (ESKD) is responsible for high mortality and significant healthcare costs, with the number of cases sharply increasing in the past 2 decades. In this study, we aimed to reduce these impacts by developing an ESKD model for predicting its occurrence in a 2-year period.

Materials and methods

We developed a machine learning (ML) pipeline to test different models for the prediction of ESKD. The electronic health record was used to capture several kidney disease-related variables. Various imputation methods, feature selection, and sampling approaches were tested. We compared the performance of multiple ML models using area under the ROC curve (AUCROC), area under the Precision-Recall curve (PR-AUC), and Brier scores for dis

SUBMITTER: Petousis P 

PROVIDER: S-EPMC10898824 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature