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使用机器学习概率来识别COVID-19的影响

Vijendra Ramlall^1,2,3,4, Undina Gisladottir^1,2,3, Jenna Kefeli²

¹Department of Biomedical Informatics, Columbia University, Columbia University Irving Medical Center, New York, NY 10032, USA.

Patterns (New York, N.Y.)

|December 18, 2023

概括

这项研究开发了一种机器学习模型,以准确地识别患者记录中的COVID-19诊断. 较高的COVID-19诊断概率与心肌梗塞,尿路感染,急性功能衰竭和2型糖尿病的风险增加有关.

科学领域:

传染性疾病传染性疾病
流行病学流行病学
医疗信息学医疗信息学

背景情况:

新型冠状病毒疾病2019 (COVID-19) 已经造成了全球健康和经济上的重大影响.
电子健康记录 (EHR) 的回顾性分析对于了解COVID-19的长期影响至关重要.
不完整的EHR在准确识别COVID-19病例以进行分析方面带来了挑战.

研究的目的:

开发一种强大的方法来识别患者电子健康记录中的COVID-19诊断.
调查COVID-19确诊病例与随后出现特定健康状况之间的关联.

主要方法:

一个随机的森林分类器被训练来预测每个患者访问的COVID-19诊断的概率.
该模型利用电子健康记录数据来分配这些概率.
进行了统计分析,以将COVID-19的概率与未来的诊断相关联.

主要成果:

随机森林分类器有效地分配了患者访问期间COVID-19诊断的概率.
较高的COVID-19诊断概率与未来心肌梗塞风险增加显著相关.
增加的COVID-19概率也与更高的尿路感染,急性功能衰竭和2型糖尿病的发病率相关.

结论:

开发的分类器提高了在EHR数据中识别COVID-19病例的准确性.
COVID-19 诊断与患有严重的长期健康并发症的风险增加有关.
这种方法支持正在进行的研究,研究COVID-19的长期后果.

关键词:

COVID-19 的影响是什么?算法开发开发的发展算法临床信息学临床信息学机器学习是机器学习.生存分析,生存分析.

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