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

Abstract

ObjectivesTo 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.MethodsIn this population-based study, we combined an 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 for 2005–2013 using the eXtreme Gradient Boosting algorithm. A dataset for 2014–2015 was used to test the predictive model. The main outcome was the accuracy of the predictive model for the number of daily OHCA events, based on mean absolute error (MAE) and mean absolute percentage error (MAPE). In general, a model with MAPE less than 10% is considered highly accurate.ResultsAmong the 1 299 784 OHCA cases, 661 052 OHCA cases of cardiac origin (525 374 cases in the training dataset on which fourfold cross-validation was performed and 135 678 cases in the testing 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 (MAE 1.314 and MAPE 7.007%) and testing datasets (MAE 1.547 and MAPE 7.788%). 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.ConclusionsA ML predictive model using comprehensive daily meteorological and chronological data allows for highly precise estimates of OHCA incidence.

Promoting the Development of Astragalus mongholicus Bunge Industry in Guyang County (China) Based on MaxEnt and Remote Sensing

“…Meanwhile, the model got a Mean Absolute Percentage Error (MAPE) of 3.369%, and R 2 was 0.948. Studies have shown that when MAPE is less than 10%, the model fit is better ( Nakashima et al, 2021 ), so the ARIMA price prediction model is feasible.…”

AI and the cardiologist: when mind, heart and machine unite

“…[24][25][26] Work by Nakashima et al allowed highly precise estimates of risk for out-ofhospital cardiac arrests using a ML model. 27 Although few, studies in the in-patient setting are nevertheless promising. Zhang et al integrated AI with their hospital management system to analyse 14 clinical variables and predict in real-time the risk of major adverse cardiac events in patients presenting with chest pain.…”

Next week’s weather forecast: cloudy, cold, with a chance of cardiac arrest

“…Advancing research along these lines, in 'A machine learning model for predicting the out-of-hospital cardiac arrest using meteorological data,' Nakashima et al 3 employ a novel approach to predict the incidence of out-of-hospital cardiac arrest (OHCA) in Japan. The analysis combines data from the All-Japan Utstein Registry of OHCA cases with high-resolution meteorological and chronological data to predict daily OHCA incidence based on weather patterns.…”