Protocol for processing multivariate time-series electronic health records of COVID-19 patients

被引：0

作者：

Wang, Zixiang ^{[2
]}

Zhu, Yinghao ^{[2
]}

Sui, Dehao ^{[2
]}

Wang, Tianlong ^{[2
]}

Zhang, Yuntao ^{[2
]}

Wang, Yasha ^{[2
]}

Pan, Chengwei ^{[7
]}

Gao, Junyi ^{[3
,4
]}

Ma, Liantao ^{[2
,5
]}

Wang, Ling ^{[6
]}

Zhang, Xiaoyun ^{[1
]}

机构：

[1] Peking Univ, Sch & Hosp Stomatol, Beijing, Peoples R China

[2] Peking Univ, Beijing, Peoples R China

[3] Hlth Data Res, London, England

[4] Univ Edinburgh, Edinburgh, Scotland

[5] Peking Univ, Key Lab High Confidence Software Technol, Minist Educ, Beijing, Peoples R China

[6] Xuzhou Med Univ, Affiliated Xuzhou Municipal Hosp, Xuzhou, Jiangsu, Peoples R China

[7] Beihang Univ, Beijing, Peoples R China

来源：

STAR PROTOCOLS | 2025年 / 6卷 / 01期

基金：

北京市自然科学基金; 中国国家自然科学基金;

关键词：

D O I：

10.1016/j.xpro.2025.103669

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

The lack of standardized techniques for processing complex health data from COVID-19 patients hinders the development of accurate predictive models in healthcare. To address this, we present a protocol for utilizing real-world multivariate time-series electronic health records of COVID-19 patients. We describe steps for covering the necessary setup, data standardization, and formatting. We then provide detailed instructions for creating datasets and for training and evaluating AI models designed to predict two key outcomes: in-hospital mortality and length of stay. For complete details on the use and execution of this protocol, please refer to Gao et al.1

引用

页数：14

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