A machine learning approach to predicting inpatient mortality among pediatric acute gastroenteritis patients in Kenya.

ABSTRACT:

Background

Mortality prediction scores for children admitted with diarrhea are unavailable, early identification of at-risk patients for proper management remains a challenge. This study utilizes machine learning (ML) to develop a highly sensitive model for timelier identification of at-risk children admitted with acute gastroenteritis (AGE) for better management.

Methods

We used seven ML algorithms to build prognostic models for the prediction of mortality using de-identified data collected from children aged <5 years hospitalized with AGE at Siaya County Referral Hospital (SCRH), Kenya, between 2010 through 2020. Potential predictors included demographic, medical history, and clinical examination data collected at admission to hospital. We conducted split-sampling and empl

SUBMITTER: Ogwel B 

PROVIDER: S-EPMC12000769 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature