Machine Learning Improves Upon Clinicians' Prediction of End Stage Kidney Disease.

ABSTRACT:

Background and objectives

Chronic kidney disease progression to ESKD is associated with a marked increase in mortality and morbidity. Its progression is highly variable and difficult to predict.

Methods

This is an observational, retrospective, single-centre study. The cohort was patients attending hospital and nephrology clinic at The Canberra Hospital from September 1996 to March 2018. Demographic data, vital signs, kidney function test, proteinuria, and serum glucose were extracted. The model was trained on the featurised time series data with XGBoost. Its performance was compared against six nephrologists and the Kidney Failure Risk Equation (KFRE).

Results

A total of 12,371 patients were included, with 2,388 were found to have an adequate density (three eGFR data

SUBMITTER: Chuah A 

PROVIDER: S-EPMC8965763 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature