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

Background and aim: COVID-19 can be presented with various gastrointestinal symptoms. Shortly after the pandemic outbreak several machine learning algorithms have been implemented to assess new diagnostic and therapeutic methods for this disease. Aim of this study is to assess gas-trointestinal and liver related predictive factors for SARS-CoV-2 associated risk of hospitalization. Methods: Data collection was based on questionnaire from the COVID-19 outpatient test center and from the emergency department at the University hospital in combination with data from inter-nal hospital information system and from the mobile application used for telemedicine follow-up of patients. For statistical analysis SARS-CoV-2 negative patients were considered as controls to three different SARS-CoV-2 positive patient groups (divided based on severity of the disease). Results: Total of 710 patients were enrolled in the study. Presence of diarrhea and nausea was significantly higher in emergency department group than in the COVID-19 outpatient test center. Among liver enzymes only aspartate transaminase (AST) has been significantly elevated in the hospitalized group compared to patients discharged home. Based on random forest algorithm, AST has been identified as the most important predictor followed by age or diabetes mellitus. Diarrhea and bloating have also predictive importance although much lower than AST. Conclusion: SARS-CoV-2 positivity is connected with isolated AST elevation and the level is linked with the severity of the disease. Furthermore, using machine learning random forest algo-rithm, we have identified elevated AST as the most important predictor for COVID-19 related hos-pitalizations.