Identifying prognostic factors and developing accurate outcome predictions for in-hospital cardiac arrest by using artificial neural networks

Chung Chen-Chih; Chiu Wei-Ting; Huang Yao-Hsien; Chan Lung; Hong Chien-Tai; Chiu Hung-Wen

首页> 外文期刊>Journal of the Neurological Sciences: Official Bulletin of the World Federation of Neurology >Identifying prognostic factors and developing accurate outcome predictions for in-hospital cardiac arrest by using artificial neural networks

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Identifying prognostic factors and developing accurate outcome predictions for in-hospital cardiac arrest by using artificial neural networks

机译：通过使用人工神经网络来确定对医院内心捕杀的预后因素和发展准确的结果预测

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Background: Accurate estimation of neurological outcomes after in-hospital cardiac arrest (IHCA) provides crucial information for clinical management. This study used artificial neural networks (ANNs) to determine the prognostic factors and develop prediction models for IHCA based on immediate preresuscitation parameters.

机译：背景：准确估计院内心脏骤停（IHCA）后的神经功能预后为临床管理提供了重要信息。本研究使用人工神经网络（ANN）确定预后因素，并基于即时复苏前参数开发IHCA预测模型。

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来源
《Journal of the Neurological Sciences: Official Bulletin of the World Federation of Neurology》 |2021年第1期|共9页
作者
Chung Chen-Chih; Chiu Wei-Ting; Huang Yao-Hsien; Chan Lung; Hong Chien-Tai; Chiu Hung-Wen;
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作者单位

Taipei Med Univ Shuang Ho Hosp Dept Neurol 291 Zhongzheng Rd New Taipei 235 Taiwan;

Taipei Med Univ Shuang Ho Hosp Dept Neurol 291 Zhongzheng Rd New Taipei 235 Taiwan;

Taipei Med Univ Shuang Ho Hosp Dept Neurol 291 Zhongzheng Rd New Taipei 235 Taiwan;

Taipei Med Univ Shuang Ho Hosp Dept Neurol 291 Zhongzheng Rd New Taipei 235 Taiwan;

Taipei Med Univ Shuang Ho Hosp Dept Neurol 291 Zhongzheng Rd New Taipei 235 Taiwan;

Taipei Med Univ Coll Med Sci &

Technol Grad Inst Biomed Informat Taipei Taiwan;

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原文格式 PDF
正文语种 eng
中图分类神经病学;
关键词
Artificial neural network; Cardiac arrest; Machine learning; Outcome; Resuscitation; Prediction;

机译：人工神经网络;心脏骤停;机器学习;结果;复苏;预测;

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Identifying prognostic factors and developing accurate outcome predictions for in-hospital cardiac arrest by using artificial neural networks

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