Machine Learning Models for Predicting Pediatric Hospitalizations Due to Air Pollution and Humidity: A Retrospective Study.

Journal: Pediatric pulmonology

PMID: 40325948

Abstract

BACKGROUND: Exposure to air pollution and meteorological conditions, such as humidity, has been linked to adverse respiratory health outcomes in children. This study aims to develop predictive models for pediatric hospitalizations based on both environmental exposures and clinical features.

Authors

Zohar Barnett-Itzhaki

Public Health Services, Ministry of Health, 39 Yirmiyahu Street, 9446724, Jerusalem, Israel. zoharba@ruppin.ac.il.
Vered Nir

Department of Pediatrics, Hillel Yaffe Medical Center, Hadera, Israel.
Almog Kellner

Faculty of Engineering, Ruppin Academic Center, Emek Hefer, Israel.
Ofir Biton

Faculty of Engineering, Ruppin Academic Center, Emek Hefer, Israel.
Shir Toledano

Faculty of Engineering, Ruppin Academic Center, Emek Hefer, Israel.
Adi Klein

Department of Pediatrics, Hillel Yaffe Medical Center, Hadera, Israel.

Keywords

Adolescent Air Pollutants Air Pollution Child Child, Preschool Emergency Service, Hospital Environmental Exposure Female Hospitalization Humans Humidity Infant Machine Learning Male Retrospective Studies Seasons

External Resources

View on PubMed Access via DOI PubMed (40325948)

Machine Learning Models for Predicting Pediatric Hospitalizations Due to Air Pollution and Humidity: A Retrospective Study.

Abstract

Authors

Keywords

External Resources

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