Machine learning model for predicting out-of-hospital cardiac arrests using meteorological and chronological data.
Machine learning model for predicting out-of-hospital cardiac arrests using meteorological and chronological data.
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DOI：
10.1136/heartjnl-2020-318726
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发表时间：
2021-06-11
期刊：
影响因子：
--
通讯作者：
Nishimura K
中科院分区：
文献类型：
--
作者：
Nakashima T;Ogata S;Noguchi T;Tahara Y;Onozuka D;Kato S;Yamagata Y;Kojima S;Iwami T;Sakamoto T;Nagao K;Nonogi H;Yasuda S;Iihara K;Neumar R;Nishimura K
使用一套机器学习(ML)方法和高分辨率的气象和年代学数据，评估用于稳健估计每日院外心脏骤停(UchA)发生率的预测模型。在这项以人口为基础的研究中，我们结合了联合国人权高专办全国范围的登记和来自日本的高分辨率气象和年代学数据。我们利用2005-2013年的训练数据集，使用极端梯度增强算法开发了一个模型来预测每天的uchA发病率。2014-2015年的数据集被用来检验预测模型。主要结果是根据平均绝对误差(MAE)和平均绝对百分比误差(MAPE)建立的预测模型的准确性。一般来说，MAPE小于10%的模型被认为是高度精确的。在1 299 784例uchA病例中，661 052例来自心脏(在进行了四次交叉验证的训练数据集中的525 374例和在测试数据集中的135 678例)进入分析。与单独使用气象或时间变量的最大似然模型相比，结合气象和时间变量的最大似然模型在训练数据集(MAE 1.314和MAPE 7.007%)和测试数据集(MAE 1.547和MAPE 7.788%)中具有最高的预测精度。周日、周一、节假日、冬季、较低的环境温度和较大的日间或日内温差比其他气象和年代学变量更易与uchA的发生相关。使用综合的每日气象和年表数据的最大似然预测模型可以非常精确地估计出ol A的发病率。
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. In 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. Among 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. A ML predictive model using comprehensive daily meteorological and chronological data allows for highly precise estimates of OHCA incidence.
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DOI：
10.1111/j.1440-1681.1984.tb00254.x
发表时间：
1984-01-01
影响因子：
2.9
作者：
HIRAMATSU, K;YAMADA, T;KATAKURA, M
通讯作者：
KATAKURA, M
影响因子：
3.2
作者：
Chen, Yao;Wang, Xiao;Adibuzzaman, Mohammad
通讯作者：
Adibuzzaman, Mohammad
影响因子：
3.6
作者：
Nakanishi, Naohiko;Nishizawa, Shinya;Matsubara, Hiroaki
通讯作者：
Matsubara, Hiroaki
影响因子：
39.3
作者：
Al'Arefilb, Subhi J.;Maliakal, Gabriel;Shaw, Leslee J.
通讯作者：
Shaw, Leslee J.
影响因子：
168.9
作者：
Nakashima, Takahiro;Noguchi, Teruo;Tsutsui, Hiroyuki
通讯作者：
Tsutsui, Hiroyuki