Development and evaluation of a machine learning model predicting out-of-hospital cardiac arrest using environmental factors.

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Title: Development and evaluation of a machine learning model predicting out-of-hospital cardiac arrest using environmental factors.
Authors: Nakashima T; Department of Emergency Medicine and the Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor, MI, USA. takana@med.umich.edu.; Department of Cardiovascular Medicine, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan. takana@med.umich.edu.; Department of Preventive Medicine and Epidemiology, National Cerebral and Cardiovascular Centre, Suita, Japan. takana@med.umich.edu., Ogata S; Department of Preventive Medicine and Epidemiology, National Cerebral and Cardiovascular Centre, Suita, Japan., Kiyoshige E; Department of Preventive Medicine and Epidemiology, National Cerebral and Cardiovascular Centre, Suita, Japan., Al-Hamdan MZ; National Center for Computational Hydroscience and Engineering, School of Engineering, University of Mississippi, Oxford, MS, USA.; Department of Civil Engineering, School of Engineering, University of Mississippi, Oxford, MS, USA., Wang Y; Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA., Noguchi T; Department of Cardiovascular Medicine, National Cerebral and Cardiovascular Centre, Suita, Japan., Shields TA; Department of Emergency Medicine and the Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor, MI, USA., Al-Araji R; Emory University, Woodruff Health Sciences Center Atlanta, Atlanta, GA, USA., McNally B; Emory University School of Medicine, Atlanta, GA, USA., Nishimura K; Department of Preventive Medicine and Epidemiology, National Cerebral and Cardiovascular Centre, Suita, Japan., Neumar RW; Department of Emergency Medicine and the Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor, MI, USA.
Source: NPJ digital medicine [NPJ Digit Med] 2025 Dec 22; Vol. 8 (1), pp. 789. Date of Electronic Publication: 2025 Dec 22.
Publication Type: Journal Article
Journal Info: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101731738 Publication Model: Electronic Cited Medium: Internet ISSN: 2398-6352 (Electronic) Linking ISSN: 23986352 NLM ISO Abbreviation: NPJ Digit Med Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2398-6352
DOI:10.1038/s41746-025-02235-4