A machine learning approach for identification of gastrointestinal predictors for the risk of COVID-19 related hospitalization.

ABSTRACT:

Background and aim

COVID-19 can be presented with various gastrointestinal symptoms. Shortly after the pandemic outbreak, several machine learning algorithms were implemented to assess new diagnostic and therapeutic methods for this disease. The aim of this study is to assess gastrointestinal and liver-related predictive factors for SARS-CoV-2 associated risk of hospitalization.

Methods

Data collection was based on a questionnaire from the COVID-19 outpatient test center and from the emergency department at the University Hospital in combination with the data from internal hospital information system and from a mobile application used for telemedicine follow-up of patients. For statistical analysis SARS-CoV-2 negative patients were considered as controls in three different S

SUBMITTER: Liptak P 

PROVIDER: S-EPMC8944335 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature