TY - JOUR

T1 - Artificial intelligence-driven prediction of COVID-19-related hospitalization and death

T2 - a systematic review

AU - Shakibfar, Saeed

AU - Nyberg, Fredrik

AU - Li, Huiqi

AU - Zhao, Jing

AU - Nordeng, Hedvig Marie Egeland

AU - Sandve, Geir Kjetil Ferkingstad

AU - Pavlovic, Milena

AU - Hajiebrahimi, Mohammadhossein

AU - Andersen, Morten

AU - Sessa, Maurizio

N1 - Funding Information: This work was performed as part of the Nordic COHERENCE Project, Project No. 105670 funded by NordForsk under the Nordic Council of Ministers and the EU-COVID-19 Project, Project No. 312707 funded by the Norwegian Research Council and by a grant from the Novo Nordisk Foundation to the University of Copenhagen (NNF15SA0018404). The Swedish SCIFI-PEARL project has received basic funding from the Swedish state under the agreement between the Swedish government and the county councils, the ALF-agreement (Avtal om Läkarutbildning och Forskning/Medical Training and Research Agreement) grants ALFGBG-938453, ALFGBG-971130, ALFGBG-978954 and during 2020–2021 had funding from FORMAS (Forskningsrådet för miljö, areella näringar och samhällsbyggande/Research Council for Environment, Agricultural Sciences and Spatial Planning), a Government Research Council for Sustainable Development, Grant 2020-02828. Additional grants supporting different aspects of ongoing research within the study include: the Swedish Heart Lung Foundation (20210030 and 2021-0581), grants from the SciLifeLab National COVID-19 Research Program, financed by the Knut och Alice Wallenberg Foundation (KAW 2020.0299), the Swedish Research Council (2021-05045, 2021-05450), the Swedish Social Insurance Agency (FK 2021/011186) and Forte (Swedish Research Council for Health, Working Life and Welfare), grant 2022-00444.

PY - 2023

Y1 - 2023

N2 - Aim: 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. Study eligibility criteria: 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. Data sources: Articles recorded in Ovid MEDLINE from 01/01/2019 to 22/08/2022 were screened. Data extraction: We extracted information on data sources, AI models, and epidemiological aspects of retrieved studies. Bias assessment: A bias assessment of AI models was done using PROBAST. Participants: Patients tested positive for COVID-19. Results: 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 <5,000. Data collection generally included information on demographics, clinical records, laboratory results, and pharmacological treatments (i.e., high-dimensional datasets). In most studies, the models were internally validated with cross-validation, but the majority of studies lacked external validation and calibration. Covariates were not prioritized using ensemble approaches in most of the studies, however, models still showed moderately good performances with Area under the Receiver operating characteristic Curve (AUC) values >0.7. According to the assessment with PROBAST, all models had a high risk of bias and/or concern regarding applicability. Conclusions: 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.

AB - Aim: 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. Study eligibility criteria: 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. Data sources: Articles recorded in Ovid MEDLINE from 01/01/2019 to 22/08/2022 were screened. Data extraction: We extracted information on data sources, AI models, and epidemiological aspects of retrieved studies. Bias assessment: A bias assessment of AI models was done using PROBAST. Participants: Patients tested positive for COVID-19. Results: 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 <5,000. Data collection generally included information on demographics, clinical records, laboratory results, and pharmacological treatments (i.e., high-dimensional datasets). In most studies, the models were internally validated with cross-validation, but the majority of studies lacked external validation and calibration. Covariates were not prioritized using ensemble approaches in most of the studies, however, models still showed moderately good performances with Area under the Receiver operating characteristic Curve (AUC) values >0.7. According to the assessment with PROBAST, all models had a high risk of bias and/or concern regarding applicability. Conclusions: 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.

KW - AI

KW - bias

KW - COVID-19

KW - pharmacoepidemiology

KW - predictive modeling

KW - PROBAST

U2 - 10.3389/fpubh.2023.1183725

DO - 10.3389/fpubh.2023.1183725

M3 - Review

C2 - 37408750

AN - SCOPUS:85164464175

VL - 11

JO - Frontiers in Public Health

JF - Frontiers in Public Health

SN - 2296-2565

M1 - 1183725

ER -