Development of a Predictive Model for Survival Over Time in Patients With Out-of-Hospital Cardiac Arrest Using Ensemble-Based Machine Learning.

IF 1.3 4区 医学 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Hong-Jae Choi, Changhee Lee, JinHo Chun, Roma Seol, Yun Mi Lee, Youn-Jung Son
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引用次数: 0

Abstract

As of now, a model for predicting the survival of patients with out-of-hospital cardiac arrest has not been established. This study aimed to develop a model for identifying predictors of survival over time in patients with out-of-hospital cardiac arrest during their stay in the emergency department, using ensemble-based machine learning. A total of 26 013 patients from the Korean nationwide out-of-hospital cardiac arrest registry were enrolled between January 1 and December 31, 2019. Our model, comprising 38 variables, was developed using the Survival Quilts model to improve predictive performance. We found that changes in important variables of patients with out-of-hospital cardiac arrest were observed 10 minutes after arrival at the emergency department. The important score of the predictors showed that the influence of patient age decreased, moving from the highest rank to the fifth. In contrast, the significance of reperfusion attempts increased, moving from the fourth to the highest rank. Our research suggests that the ensemble-based machine learning model, particularly the Survival Quilts, offers a promising approach for predicting survival in patients with out-of-hospital cardiac arrest. The Survival Quilts model may potentially assist emergency department staff in making informed decisions quickly, reducing preventable deaths.

利用基于集合的机器学习技术开发院外心脏骤停患者随时间变化的存活率预测模型。
迄今为止,预测院外心脏骤停患者存活率的模型尚未建立。本研究旨在利用基于集合的机器学习方法建立一个模型,以确定院外心脏骤停患者在急诊科住院期间的存活率预测因素。在2019年1月1日至12月31日期间,韩国全国院外心脏骤停登记处共登记了26 013名患者。我们的模型由 38 个变量组成,采用生存棉被模型开发,以提高预测性能。我们发现,院外心脏骤停患者的重要变量在到达急诊科 10 分钟后发生了变化。预测因子的重要得分显示,患者年龄的影响力有所下降,从最高级别降至第五位。相比之下,再灌注尝试的重要性则有所上升,从第四位上升到最高位。我们的研究表明,基于集合的机器学习模型,尤其是 "生存之被(Survival Quilts)",为预测院外心脏骤停患者的存活率提供了一种很有前景的方法。生存之被(Survival Quilts)模型有可能帮助急诊科工作人员迅速做出明智的决定,从而减少可预防的死亡。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Cin-Computers Informatics Nursing
Cin-Computers Informatics Nursing 工程技术-护理
CiteScore
2.00
自引率
15.40%
发文量
248
审稿时长
6-12 weeks
期刊介绍: For over 30 years, CIN: Computers, Informatics, Nursing has been at the interface of the science of information and the art of nursing, publishing articles on the latest developments in nursing informatics, research, education and administrative of health information technology. CIN connects you with colleagues as they share knowledge on implementation of electronic health records systems, design decision-support systems, incorporate evidence-based healthcare in practice, explore point-of-care computing in practice and education, and conceptually integrate nursing languages and standard data sets. Continuing education contact hours are available in every issue.
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