Work

Artificial intelligence platform to predict children's hospital care for respiratory disease using clinical, pollution, and climatic factors

Year: 2025

Type: article

Abstract: Background: Hospitals and health care systems may benefit from artificial intelligence (AI) and big data to analyse clinical information combined with external sources. Machine learning, a subset of AI, uses algorithms trained on data to generate predictive models. Air pollution is a known risk factor for various health outcomes, with children being a particularly vulnerable group. Methods: This study developed and validated an AI-based platform to forecast paediatric emergency visits and hospital admissions for respiratory diseases, using clinical and environmental data in the Metropolitan Area of São Paulo, Brazil. We applied XGBoost, a tree-based machine learning algorithm, to predict hospital use at Sabará Children's Hospital, incorporating clinical, pollution, and climatic variables. Results: ) concentrations near children's residences on the day of hospital arrival. In contrast, admissions were linked to lower relative humidity, particularly on drier days. Additional associations were found between admissions and the spring season, as well as male sex. Conclusions: We developed a platform that integrates clinical and environmental databases within a big data framework to process and analyse information using AI techniques. This tool predicts daily emergency department and hospital admission flows related to paediatric respiratory diseases. The algorithms can distinguish whether a child arriving at the emergency department is likely to be treated and discharged or will require hospital admission. This predictive capability may support hospital planning and resource allocation, particularly in contexts of environmental vulnerability.

Source: Journal of Global Health

DOI: 10.7189/jogh.15.04207

Authors: William Cabral-Miranda, Cauê Beloni, Felipe Lora, Rogério Afonso, Thales Araújo, Fátima Fernandes

Institution: Instituto Pensi

Language: English

Topic: Air Quality and Health Impacts

Subfield: Health, Toxicology and Mutagenesis

Field: Environmental Science

Domain: Physical Sciences

SDGs: Good health and well-being, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,200