Artificial intelligence-driven prediction of COVID-19-related hospitalization and death: a systematic review

• Published in: Frontiers in public health 2023, Vol.11, p.1183725-1183725
• Main Authors: Shakibfar, Saeed, Nyberg, Fredrik, Li, Huiqi, Zhao, Jing, Nordeng, Hedvig Marie Egeland, Sandve, Geir Kjetil Ferkingstad, Pavlovic, Milena, Hajiebrahimi, Mohammadhossein, Andersen, Morten, Sessa, Maurizio
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; bias; COVID-19; COVID-19 - epidemiology; Environmental & Occupational Health; Folkhälsovetenskap, global hälsa, socialmedicin och epidemiologi; Hospitalization; Humans; Infectious Medicine; Infektionsmedicin; Language; pharmacoepidemiology; predictive modeling; Probability Theory and Statistics; PROBAST; Public; Public Health; Public Health, Global Health, Social Medicine and Epidemiology; risk; ROC Curve; Sannolikhetsteori och statistik
• ISSN: 2296-2565; 2296-2565
• DOI: 10.3389/fpubh.2023.1183725

To perform a systematic review on the use of Artificial Intelligence (AI) techniques for predicting COVID-19 hospitalization and mortality using primary and secondary data sources. Cohort, clinical trials, meta-analyses, and observational studies investigating COVID-19 hospitalization or mortality using artificial intelligence techniques were eligible. Articles without a full text available in the English language were excluded. Articles recorded in Ovid MEDLINE from 01/01/2019 to 22/08/2022 were screened. We extracted information on data sources, AI models, and epidemiological aspects of retrieved studies. A bias assessment of AI models was done using PROBAST. Patients tested positive for COVID-19. We included 39 studies related to AI-based prediction of hospitalization and death related to COVID-19. The articles were published in the period 2019-2022, and mostly used Random Forest as the model with the best performance. AI models were trained using cohorts of individuals sampled from populations of European and non-European countries, mostly with cohort sample size 0.7. According to the assessment with PROBAST, all models had a high risk of bias and/or concern regarding applicability. A broad range of AI techniques have been used to predict COVID-19 hospitalization and mortality. The studies reported good prediction performance of AI models, however, high risk of bias and/or concern regarding applicability were detected.