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Machine learning algorithms for predicting days of high incidence for out-of-hospital cardiac arrest

Machine learning algorithms for predicting days of high incidence for out-of-hospital cardiac arrest

Authors :
Kaoru Shimada-Sammori
Tadanaga Shimada
Rie E. Miura
Rui Kawaguchi
Yasuo Yamao
Taku Oshima
Takehiko Oami
Keisuke Tomita
Koichiro Shinozaki
Taka-aki Nakada
Source :
Scientific Reports, Vol 13, Iss 1, Pp 1-8 (2023)
Publication Year :
2023
Publisher :
Nature Portfolio, 2023.

Abstract

Abstract Predicting out-of-hospital cardiac arrest (OHCA) events might improve outcomes of OHCA patients. We hypothesized that machine learning algorithms using meteorological information would predict OHCA incidences. We used the Japanese population-based repository database of OHCA and weather information. The Tokyo data (2005–2012) was used as the training cohort and datasets of the top six populated prefectures (2013–2015) as the test. Eight various algorithms were evaluated to predict the high-incidence OHCA days, defined as the daily events exceeding 75% tile of our dataset, using meteorological and chronological values: temperature, humidity, air pressure, months, days, national holidays, the day before the holidays, the day after the holidays, and New Year’s holidays. Additionally, we evaluated the contribution of each feature by Shapley Additive exPlanations (SHAP) values. The training cohort included 96,597 OHCA patients. The eXtreme Gradient Boosting (XGBoost) had the highest area under the receiver operating curve (AUROC) of 0.906 (95% confidence interval; 0.868–0.944). In the test cohorts, the XGBoost algorithms also had high AUROC (0.862–0.923). The SHAP values indicated that the “mean temperature on the previous day” impacted the most on the model. Algorithms using machine learning with meteorological and chronological information could predict OHCA events accurately.

Subjects

Subjects :
Medicine
Science

Details

Language :
English
ISSN :
20452322
Volume :
13
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Scientific Reports
Publication Type :
Academic Journal
Accession number :
edsdoj.06db531055b84a178b9099bfada32b25
Document Type :
article
Full Text :
https://doi.org/10.1038/s41598-023-36270-6