A predictive paradigm for COVID-19 prognosis based on the longitudinal measure of biomarkers.

ABSTRACT: Novel coronavirus disease 2019 (COVID-19) is an emerging, rapidly evolving crisis, and the ability to predict prognosis for individual COVID-19 patient is important for guiding treatment. Laboratory examinations were repeatedly measured during hospitalization for COVID-19 patients, which provide the possibility for the individualized early prediction of prognosis. However, previous studies mainly focused on risk prediction based on laboratory measurements at one time point, ignoring disease progression and changes of biomarkers over time. By using historical regression trees (HTREEs), a novel machine learning method, and joint modeling technique, we modeled the longitudinal trajectories of laboratory biomarkers and made dynamically predictions on individual prognosis for 1997 COVID-19 pati

SUBMITTER: Chen X 

PROVIDER: S-EPMC8195146 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature