Multi-perspective predictive modeling for acute kidney injury in general hospital populations using electronic medical records.

ABSTRACT:

Objectives

Acute kidney injury (AKI) in hospitalized patients puts them at much higher risk for developing future health problems such as chronic kidney disease, stroke, and heart disease. Accurate AKI prediction would allow timely prevention and intervention. However, current AKI prediction researches pay less attention to model building strategies that meet complex clinical application scenario. This study aims to build and evaluate AKI prediction models from multiple perspectives that reflect different clinical applications.

Materials and methods

A retrospective cohort of 76 957 encounters and relevant clinical variables were extracted from a tertiary care, academic hospital electronic medical record (EMR) system between November 2007 and December 2016. Five machine learn

SUBMITTER: He J 

PROVIDER: S-EPMC6447093 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature