Machine learning with environmental predictors to forecast hospital visits and admissions: a systematic review
2025
Levi Monteiro Martins | Elsa Coz | Delphine Maucort-Boulch | Mohand-Saïd Hacid
Abstract Many studies have demonstrated a correlation between environmental monitoring data and healthcare service demand, highlighting its contribution to the global problem of emergency department crowding. To address this problem, forecasting models are essential for resource allocation and general management to improve patient outcomes. Machine Learning (ML), especially Deep Learning (DL), offers promise for forecasting patient volume. In this work, we present a systematic review of the use of ML to predict the health impacts of environmental exposures in the context of hospital visits and admissions. Standardized tools for conducting a systematic review were used, including the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies (CHARMS), and Prediction model Risk Of Bias ASsessment Tool (PROBAST). The search included studies from 2012 to 2025. We focus on answering how ML has been applied and what the major environmental predictors are used. As a result, 36 studies were retained from PubMed, Embase, and IEEE Xplore databases. We found that many studies exhibited a high risk of bias due to poor handling of missing values, inadequate outcome definitions, biased participant selection, and a low number of events per variable. Additionally, we found that the most used air pollutants and meteorological variables were PM2.5, PM10, NO2, SO2, CO, O3, and temperature. Furthermore, the most common models were Random Forest and feed-forward neural networks. In addition, land use, remote sensing, demographic, and socioeconomic data offer promising avenues for improving model performance.
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