Machine learning for predicting clinical outcomes of hospitalised children: a systematic review of applications in low- and middle-income countries.

ABSTRACT:

Background

Machine Learning (ML) can contribute to reducing child mortality and morbidity in low- and middle-income countries (LMICs), yet its development and clinical adoption remain unclear. This systematic review provides an overview of ML for hospitalised children in LMICs.

Methods

In June 2025, searches in five scientific databases and one scholarly search engine identified 26 eligible peer-reviewed studies using ML on hospitalised children under 18. Studies using only conventional statistics and perinatal data were excluded. Study quality and bias were assessed using PROBAST + AI. Descriptive statistics were used for data analysis. PRISMA reporting guideline was followed.

Findings

These studies were conducted in Asia (58%) and Sub-Saharan Africa (38%), mostly re

SUBMITTER: Nkhono W 

PROVIDER: S-EPMC12818084 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature