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

ABSTRACT:

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 mortality in COVID-19 patients.

Methods

This retrospective study consisted of 5,766 persons-under-investigation for COVID-19 between 7 February 2020 and 4 May 2020. Demographics, chronic comorbidities, vital signs, symptoms and laboratory tests at admission were collected. A deep neural network model and a risk-score system were constructed to predict ICU admission and in-hospital mortality. Prediction performance used the receiver operating characteristic area under the curve (AUC).

Results

The top ICU predictors were procalcitonin, lactate dehydrogenase, C-reactive protein, ferritin and oxygen sa

SUBMITTER: Li X 

PROVIDER: S-EPMC7651477 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature