Feature Ranking in Predictive Models for Hospital-Acquired Acute Kidney Injury.

ABSTRACT: Acute Kidney Injury (AKI) is a common complication encountered among hospitalized patients, imposing significantly increased cost, morbidity, and mortality. Early prediction of AKI has profound clinical implications because currently no treatment exists for AKI once it develops. Feature selection (FS) is an essential process for building accurate and interpretable prediction models, but to our best knowledge no study has investigated the robustness and applicability of such selection process for AKI. In this study, we compared eight widely-applied FS methods for AKI prediction using nine-years of electronic medical records (EMR) and examined heterogeneity in feature rankings produced by the methods. FS methods were compared in terms of stability with respect to data sampling variation, sim

SUBMITTER: Wu L 

PROVIDER: S-EPMC6251919 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature