Recurrent neural network models (CovRNN) for predicting outcomes of patients with COVID-19 on admission to hospital: model development and validation using electronic health record data-Reference-Cited by-同舟云学术

Recurrent neural network models (CovRNN) for predicting outcomes of patients with COVID-19 on admission to hospital: model development and validation using electronic health record data

Published:2022-06 Issue:6 Volume:4 Page:e415-e425
ISSN:2589-7500
Container-title:The Lancet Digital Health
language:en
Short-container-title:The Lancet Digital Health

Author:

Rasmy Laila,Nigo Masayuki,Kannadath Bijun Sai,Xie Ziqian,Mao Bingyu,Patel Khush,Zhou Yujia,Zhang Wanheng,Ross Angela,Xu Hua,Zhi Degui

Publisher

Elsevier BV

Subject

Health Information Management,Decision Sciences (miscellaneous),Health Informatics,Medicine (miscellaneous)

Reference24 articles.

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4. Real-time prediction of COVID-19 related mortality using electronic health records;Schwab;Nat Commun,2021

5. The development and validation of simplified machine learning algorithms to predict prognosis of hospitalized patients with COVID-19: multicenter, retrospective study;He;J Med Internet Res,2022

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