This paper is in the following e-collection/theme issue:

JMIR Theme Issue: COVID-19 Special Issue (2495) Clinical Informatics (1164) Clinical Information and Decision Making (1661) Personal Health Records, Patient-Accessible Electronic Health Records, Patient Portals (636) Machine Learning (1741) Artificial Intelligence (1745) Nursing Records (17) Infectious Diseases (non-STD/STI) (1658) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1531)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/53343, first published .
Identification of Predictors for Clinical Deterioration in Patients With COVID-19 via Electronic Nursing Records: Retrospective Observational Study

Identification of Predictors for Clinical Deterioration in Patients With COVID-19 via Electronic Nursing Records: Retrospective Observational Study

Identification of Predictors for Clinical Deterioration in Patients With COVID-19 via Electronic Nursing Records: Retrospective Observational Study

Authors of this article:

Sumi Sung1 Author Orcid Image ;   Youlim Kim2 Author Orcid Image ;   Su Hwan Kim3 Author Orcid Image ;   Hyesil Jung4 Author Orcid Image
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New in JMIR: Identification of Predictors for Clinical Deterioration in ##Patients With #COVID19 #coronavirus via Electronic Nursing Records: Retrospective Observational #Study https://t.co/kXfGIauz1H https://t.co/qkqLALXvmA

3:58 PM · Mar 29, 2024

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RT @jmirpub: New in JMIR: Identification of Predictors for Clinical Deterioration in ##Patients With #COVID19 #coronavirus via Electronic N…

3:58 PM · Mar 29, 2024

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