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

• Published in: PeerJ (San Francisco, CA) CA), 2022-03, Vol.10, p.e13124-e13124, Article e13124
• Main Authors: Lipták, Peter, Banovcin, Peter, Rosoľanka, Róbert, Prokopič, Michal, Kocan, Ivan, Žiačiková, Ivana, Uhrik, Peter, Grendar, Marian, Hyrdel, Rudolf
• Format: Article
• Language: English
• Subjects: Abdomen; Age; Algorithms; Artificial intelligence; Aspartate transaminase; Communications software; Constipation; Coronaviruses; COVID-19; COVID-19 - epidemiology; Data collection; Data mining; Data Mining and Machine Learning; Diabetes; Diabetes mellitus; Diarrhea; Emergency medical care; Epidemics; Fever; Gastroenterology and Hepatology; Gastroesophageal reflux; Gender; Health aspects; Hospitalization; Hospitals; Humans; Hypertension; Hypotheses; Infectious Diseases; Intensive care; Laboratories; Learning algorithms; Liver; Liver diseases; Machine Learning; Medical research; Medicine, Experimental; Nausea; Pain; Patients; Pest outbreaks; Questionnaires; Random forest; SARS-CoV-2; Severe acute respiratory syndrome coronavirus 2; Statistical analysis; Symptoms; Taiwan; Transaminase
• ISSN: 2167-8359; 2167-8359
• DOI: 10.7717/peerj.13124

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. 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 SARS-CoV-2 positive patient groups (divided based on severity of the disease). The data were visualized and analyzed in R version 4.0.5. The Chi-squared or Fisher test was applied to test the null hypothesis of independence between the factors followed, where appropriate, by the multiple comparisons with the Benjamini Hochberg adjustment. The null hypothesis of the equality of the population medians of a continuous variable was tested by the Kruskal Wallis test, followed by the Dunn multiple comparisons test. In order to assess predictive power of the gastrointestinal parameters and other measured variables for predicting an outcome of the patient group the Random Forest machine learning algorithm was trained on the data. The predictive ability was quantified by the ROC curve, constructed from the Out-of-Bag data. Matthews correlation coefficient was used as a one-number summary of the quality of binary classification. The importance of the predictors was measured using the Variable Importance. A 2D representation of the data was obtained by means of Principal Component Analysis for mixed type of data. Findings with the -value below 0.05 were considered statistically significant. A total of 710 patients were enrolled in the study. The presence of diarrhea and nausea was significantly higher in the 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 the 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 l