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

Theme Issue: Medical Informatics and COVID-19 (97) JMIR Theme Issue: COVID-19 Special Issue (1993) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1296)

Published on in Vol 8 , No 9 (2020) :September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/19588, first published .
Nomogram for Predicting COVID-19 Disease Progression Based on Single-Center Data: Observational Study and Model Development

Nomogram for Predicting COVID-19 Disease Progression Based on Single-Center Data: Observational Study and Model Development

Nomogram for Predicting COVID-19 Disease Progression Based on Single-Center Data: Observational Study and Model Development

Authors of this article:

Tao Fan 1 Author Orcid Image ;   Bo Hao 1 Author Orcid Image ;   Shuo Yang 1 Author Orcid Image ;   Bo Shen 1 Author Orcid Image ;   Zhixin Huang 1 Author Orcid Image ;   Zilong Lu 1 Author Orcid Image ;   Rui Xiong 1 Author Orcid Image ;   Xiaokang Shen 1 Author Orcid Image ;   Wenyang Jiang 1 Author Orcid Image ;   Lin Zhang 1 Author Orcid Image ;   Donghang Li 1 Author Orcid Image ;   Ruyuan He 1 Author Orcid Image ;   Heng Meng 1 Author Orcid Image ;   Weichen Lin 1 Author Orcid Image ;   Haojie Feng 1 Author Orcid Image ;   Qing Geng 1 Author Orcid Image
Article Authors Cited by (2) Tweetations (2) Metrics

Journals

  1. Chen X, Peng F, Zhou X, Zhu J, Chen X, Gong Y, Shupeng W, Niu W. Predicting severe or critical symptoms in hospitalized patients with COVID-19 from Yichang, China. Aging 2021;13(2):1608 View
  2. Karaismailoglu E, Karaismailoglu S. Two novel nomograms for predicting the risk of hospitalization or mortality due to COVID‐19 by the naïve Bayesian classifier method. Journal of Medical Virology 2021;93(5):3194 View