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

## TL;DR

This review explores how machine learning is used to predict outcomes for hospitalized children in low- and middle-income countries, highlighting its potential and challenges.

## Contribution

The study systematically reviews ML applications for hospitalized children in LMICs, identifying trends and barriers to clinical adoption.

## Key findings

- Most studies focused on mortality prediction using patient files and ensemble methods.
- The median AUROC of ML models was 0.81, indicating strong predictive performance.
- High-quality data and alignment with local clinical needs are critical for successful implementation.

## Abstract

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.

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.

These studies were conducted in Asia (58%) and Sub-Saharan Africa (38%), mostly retrospective (62%), and predominantly used patient files (62%). The median sample size was 1291. Prognostic models dominated (69%), primarily targeting mortality (50%). Ensemble methods were most common (50%). The median AUROC was 0.81 (IQR 0.78–0.83). Most models were at a clinical Readiness Level 3–4 (81%). Barriers and facilitators related to data (65%, 34% respectively), implementation (50%, 77%), technology (31%, 42%), and human (19%, 35%) were reported.

We provided evidence of ML's promising performance for LMICs. Mortality prediction was the main focus. Arriving at clinical applications that benefit LMICs, requires investment in high-quality data and alignment to local (clinical) needs.

This project is part of the EDCTP2 programme (grant number RIA2020I-3294 IMPALA) supported by the European Union.