Machine learning model for predicting out-of-hospital cardiac arrests using meteorological and chronological data

OBJECTIVES: To evaluate a predictive model for robust estimation of daily out-of-hospital cardiac arrest (OHCA) incidence using a suite of machine learning (ML) approaches and high-resolution meteorological and chronological data. METHODS: In this population-based study, we combined an OHCA nationwi...

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Detalles Bibliográficos
Autores principales: Nakashima, Takahiro, Ogata, Soshiro, Noguchi, Teruo, Tahara, Yoshio, Onozuka, Daisuke, Kato, Satoshi, Yamagata, Yoshiki, Kojima, Sunao, Iwami, Taku, Sakamoto, Tetsuya, Nagao, Ken, Nonogi, Hiroshi, Yasuda, Satoshi, Iihara, Koji, Neumar, Robert, Nishimura, Kunihiro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2021
Materias:
Healthcare Delivery, Economics and Global Health
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8223656/
https://www.ncbi.nlm.nih.gov/pubmed/34001636
http://dx.doi.org/10.1136/heartjnl-2020-318726

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8223656/
https://www.ncbi.nlm.nih.gov/pubmed/34001636
http://dx.doi.org/10.1136/heartjnl-2020-318726

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