Multidimensional dynamic prediction model for hospitalized patients with the omicron variant in China.

ABSTRACT:

Purpose

To establish dynamic prediction models by machine learning using daily multidimensional data for coronavirus disease 2019 (COVID-19) patients.

Methods

Hospitalized COVID-19 patients at Peking Union Medical College Hospital from Nov 2nd, 2022, to Jan 13th, 2023, were enrolled in this study. The outcome was defined as deterioration or recovery of the patient's condition. Demographics, comorbidities, laboratory test results, vital signs, and treatments were used to train the model. To predict the following days, a separate XGBoost model was trained and validated. The Shapley additive explanations method was used to analyze feature importance.

Results

A total of 995 patients were enrolled, generating 7228 and 3170 observations for each prediction model. In the det

SUBMITTER: Chen Y 

PROVIDER: S-EPMC10579104 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature