Rafael S Costa 1,2 , Rui Henriques 3,4 , André Patrício 3 Show Affiliations »
Abstract
BACKGROUND: In the face of the current COVID-19 pandemic, the timely prediction of upcoming medical needs for infected individuals enables better and quicker care provision when necessary and management decisions within health care systems. OBJECTIVE: This work aims to predict the medical needs (hospitalizations, intensive care unit admissions, and respiratory assistance) and survivability of individuals testing positive for SARS-CoV-2 infection in Portugal. METHODS: A retrospective cohort of 38,545 infected individuals during 2020 was used. Predictions of medical needs were performed using state-of-the-art machine learning approaches at various stages of a patient's cycle, namely, at testing (prehospitalization), at posthospitalization, and during postintensive care. A thorough optimization of state-of-the-art predictors was undertaken to assess the ability to anticipate medical needs and infection outcomes using demographic and comorbidity variables, as well as dates associated with symptom onset, testing, and hospitalization. RESULTS: For the target cohort, 75% of hospitalization needs could be identified at the time of testing for SARS-CoV-2 infection. Over 60% of respiratory needs could be identified at the time of hospitalization. Both predictions had >50% precision. CONCLUSIONS: The conducted study pinpoints the relevance of the proposed predictive models as good candidates to support medical decisions in the Portuguese population, including both monitoring and in-hospital care decisions. A clinical decision support system is further provided to this end. ©André Patrício, Rafael S Costa, Rui Henriques. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 28.04.2021.
BACKGROUND: In the face of the current COVID-19 pandemic, the timely prediction of upcoming medical needs for infected individuals enables better and quicker care provision when necessary and management decisions within health care systems. OBJECTIVE: This work aims to predict the medical needs (hospitalizations, intensive care unit admissions, and respiratory assistance) and survivability of individuals testing positive for SARS-CoV-2 infection in Portugal. METHODS: A retrospective cohort of 38,545 infected individuals during 2020 was used. Predictions of medical needs were performed using state-of-the-art machine learning approaches at various stages of a patient 's cycle, namely, at testing (prehospitalization), at posthospitalization, and during postintensive care. A thorough optimization of state-of-the-art predictors was undertaken to assess the ability to anticipate medical needs and infection outcomes using demographic and comorbidity variables, as well as dates associated with symptom onset, testing, and hospitalization. RESULTS: For the target cohort, 75% of hospitalization needs could be identified at the time of testing for SARS-CoV-2 infection . Over 60% of respiratory needs could be identified at the time of hospitalization. Both predictions had >50% precision. CONCLUSIONS: The conducted study pinpoints the relevance of the proposed predictive models as good candidates to support medical decisions in the Portuguese population, including both monitoring and in-hospital care decisions. A clinical decision support system is further provided to this end. ©André Patrício, Rafael S Costa, Rui Henriques. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 28.04.2021.
Entities: Chemical
Disease
Gene
Species
Keywords:
COVID-19; clinical informatics; data modeling; intensive care admissions; machine learning; predictive models; respiratory assistance
Year: 2021
PMID： 33835931 DOI： 10.2196/26075
Source DB: PubMed Journal: J Med Internet Res ISSN： 1438-8871 Impact factor: 5.428