Characterizing the temporal changes in association between modifiable risk factors and acute kidney injury with multi-view analysis.

ABSTRACT:

Background

Acute kidney injury (AKI) is a common life-threatening clinical syndrome in hospitalized patients. Advances in machine learning has demonstrated success in AKI risk prediction using electronic health records (EHRs). However, to prevent AKI, it is critical to identify clinically modifiable factors and understand their impact at different prevention windows.

Method

We extracted 4129 clinical variables including demographics, social history, past diagnoses, procedures, labs, medications, vitals from EHRs for a cohort of 144,084 eligible inpatient encounters. We developed a multi-view learning framework for XGBoost (MV-XGB) to enhance algorithm attention on modifiable factors. To study effects of modifiable factors at different time points, we built AKI prediction mod

SUBMITTER: Liu K 

PROVIDER: S-EPMC12516820 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature