Deep‐learning artificial intelligence analysis of clinical variables predicts mortality in COVID‐19 patients

JS Zhu, P Ge, C Jiang, Y Zhang, X Li…�- Journal of the�…, 2020 - Wiley Online Library
Objective The large number of clinical variables associated with coronavirus disease 2019
(COVID‐19) infection makes it challenging for frontline physicians to effectively triage COVID�…

[HTML][HTML] Deep learning prediction of likelihood of ICU admission and mortality in COVID-19 patients using clinical variables

X Li, P Ge, J Zhu, H Li, J Graham, A Singer…�- PeerJ, 2020 - peerj.com
Background This study aimed to develop a deep-learning model and a risk-score system
using clinical variables to predict intensive care unit (ICU) admission and in-hospital�…

[HTML][HTML] Development of a severity of disease score and classification model by machine learning for hospitalized COVID-19 patients

M Marcos, M Belhassen-Garc�a, A S�nchez-Puente…�- PloS one, 2021 - journals.plos.org
Background Efficient and early triage of hospitalized Covid-19 patients to detect those with
higher risk of severe disease is essential for appropriate case management. Methods We�…

[HTML][HTML] Development of a clinical decision support system for severity risk prediction and triage of COVID-19 patients at hospital admission: an international�…

G Wu, P Yang, Y Xie, HC Woodruff…�- European�…, 2020 - Eur Respiratory Soc
Background The outbreak of coronavirus disease 2019 (COVID-19) has globally strained
medical resources and caused significant mortality. Objective To develop and validate a�…

Development and validation of prognosis model of mortality risk in patients with COVID-19

X Ma, M Ng, S Xu, Z Xu, H Qiu, Y Liu, J Lyu…�- Epidemiology &�…, 2020 - cambridge.org
This study aimed to identify clinical features for prognosing mortality risk using machine-
learning methods in patients with coronavirus disease 2019 (COVID-19). A retrospective�…

Clinical and inflammatory features based machine learning model for fatal risk prediction of hospitalized COVID-19 patients: results from a retrospective cohort study

X Guan, B Zhang, M Fu, M Li, X Yuan, Y Zhu…�- Annals of�…, 2021 - Taylor & Francis
Objectives To appraise effective predictors for COVID-19 mortality in a retrospective cohort
study. Methods A total of 1270 COVID-19 patients, including 984 admitted in Sino French�…

[HTML][HTML] Early triage of critically ill COVID-19 patients using deep learning

W Liang, J Yao, A Chen, Q Lv, M Zanin, J Liu…�- Nature�…, 2020 - nature.com
The sudden deterioration of patients with novel coronavirus disease 2019 (COVID-19) into
critical illness is of major concern. It is imperative to identify these patients early. We show�…

[HTML][HTML] Development and external evaluation of predictions models for mortality of COVID-19 patients using machine learning method

S Li, Y Lin, T Zhu, M Fan, S Xu, W Qiu, C Chen…�- Neural Computing and�…, 2023 - Springer
To predict the mortality of patients with coronavirus disease 2019 (COVID-19). We collected
clinical data of COVID-19 patients between January 18 and March 29 2020 in Wuhan�…

[HTML][HTML] Machine learning to predict mortality and critical events in a cohort of patients with COVID-19 in New York City: model development and validation

A Vaid, S Somani, AJ Russak, JK De Freitas…�- Journal of medical�…, 2020 - jmir.org
Background COVID-19 has infected millions of people worldwide and is responsible for
several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful�…

[HTML][HTML] Clinical characteristics and a decision tree model to predict death outcome in severe COVID-19 patients

Q Yang, J Li, Z Zhang, X Wu, T Liao, S Yu, Z You…�- BMC infectious�…, 2021 - Springer
Background The novel coronavirus disease 2019 (COVID-19) spreads rapidly among
people and causes a pandemic. It is of great clinical significance to identify COVID-19�…