Monitoring Approaches for a Pediatric Chronic Kidney Disease Machine Learning Model.

ABSTRACT:

Objective

The purpose of this study is to evaluate the ability of three metrics to monitor for a reduction in performance of a chronic kidney disease (CKD) model deployed at a pediatric hospital.

Methods

The CKD risk model estimates a patient's risk of developing CKD 3 to 12 months following an inpatient admission. The model was developed on a retrospective dataset of 4,879 admissions from 2014 to 2018, then run silently on 1,270 admissions from April to October, 2019. Three metrics were used to monitor its performance during the silent phase: (1) standardized mean differences (SMDs); (2) performance of a "membership model"; and (3) response distribution analysis. Observed patient outcomes for the 1,270 admissions were used to calculate prospective model performance and the

SUBMITTER: Morse KE 

PROVIDER: S-EPMC9068274 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature