A novel machine learning model for predicting the incidence of out-of-hospital cardiac arrest using weather data

2023 Fiscal Year Final Research Report

• Project/Area Number: 20K17914
• Research Category: Grant-in-Aid for Early-Career Scientists
• Allocation Type: Multi-year Fund
• Review Section: Basic Section 55060:Emergency medicine-related
• Research Institution: National Cardiovascular Center Research Institute
• Principal Investigator: Nakashima Takahiro 国立研究開発法人国立循環器病研究センター, 研究所, 客員研究員 (50796141)
• Project Period (FY): 2020-04-01 – 2024-03-31
• Keywords: 機械学習 / 予測モデル / 院外心停止 / 気象情報

Outline of Final Research Achievements

In this population-based study, we combined an out-of-hospital cardiac arrest (OHCA) nationwide registry and high-resolution meteorological and chronological datasets from Japan. We developed a model to predict daily OHCA incidence with a training dataset from 2005 to 2013 using the eXtreme Gradient Boosting algorithm. A dataset from 2014 to 2015 was used to test the predictive model.
Among the 1,299,784 OHCA cases, 661,052 OHCA cases of cardiac origin (525,374 cases in the training dataset were included in the analysis. Compared with the ML models using meteorological or chronological variables alone, the ML model with combined meteorological and chronological variables had the highest predictive accuracy in the training and testing datasets. Sunday, Monday, holiday, winter, low ambient temperature and large interday or intraday temperature difference were more strongly associated with OHCA incidence than other the meteorological and chronological variables.

Free Research Field

蘇生科学

Academic Significance and Societal Importance of the Research Achievements

本研究により複雑な気象条件と心停止発生との関連性を明確にできた。予測モデルの精度をさらに高めることで、将来的に①市民に対しては日々の気象条件に応じた注意喚起を行い、また②医療従事者に対しては限られた医療資源を日々の気象条件をもとに再配置するのに役立つことが期待される。