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<p>Development of an accurate system to predict the daily incidence of out-of-hospital cardiac arrest (OHCA) might provide a significant public health benefit. We developed a machine learning model to predict daily OHCA incidence at the regional level using high-resolution meteorological data, chronological data, and sociodemographic data. Among OHCAs of non-traumatic cause, 196,735 in the training dataset (2013-2017), 119,455 in the testing dataset (2018-2019) from internal areas, and 37,160 in the testing dataset from external areas were included in the analysis. Application of invariant causal prediction (ICP) successfully reduced the number of variables to 17 while maintaining high predictive performance for nationwide incidence per 100,000 per day in both the training and testing datasets, whether from internal or external regions, comparable to the non-ICP model using the initial 34 variables. Furthermore, the prediction model retained a satisfactory performance up to seven days in advance.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月22日

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  • Takahiro Nakashima,
  • Soshiro Ogata,
  • Eri Kiyoshige,
  • Mohammad Z. Al-Hamdan,
  • Yifan Wang,
  • Teruo Noguchi,
  • Theresa A. Shields,
  • Rabab Al-Araji,
  • Bryan McNally,
  • Kunihiro Nishimura,
  • Robert W. Neumar

摘要

Development of an accurate system to predict the daily incidence of out-of-hospital cardiac arrest (OHCA) might provide a significant public health benefit. We developed a machine learning model to predict daily OHCA incidence at the regional level using high-resolution meteorological data, chronological data, and sociodemographic data. Among OHCAs of non-traumatic cause, 196,735 in the training dataset (2013-2017), 119,455 in the testing dataset (2018-2019) from internal areas, and 37,160 in the testing dataset from external areas were included in the analysis. Application of invariant causal prediction (ICP) successfully reduced the number of variables to 17 while maintaining high predictive performance for nationwide incidence per 100,000 per day in both the training and testing datasets, whether from internal or external regions, comparable to the non-ICP model using the initial 34 variables. Furthermore, the prediction model retained a satisfactory performance up to seven days in advance.