Using Machine Learning Algorithms to Develop a Clinical Decision-Making Tool for COVID-19 Inpatients.

ABSTRACT: Within the UK, COVID-19 has contributed towards over 103,000 deaths. Although multiple risk factors for COVID-19 have been identified, using this data to improve clinical care has proven challenging. The main aim of this study is to develop a reliable, multivariable predictive model for COVID-19 in-patient outcomes, thus enabling risk-stratification and earlier clinical decision-making. Anonymised data consisting of 44 independent predictor variables from 355 adults diagnosed with COVID-19, at a UK hospital, was manually extracted from electronic patient records for retrospective, case-control analysis. Primary outcomes included inpatient mortality, required ventilatory support, and duration of inpatient treatment. Pulmonary embolism sequala was the only secondary outcome. After balancing

SUBMITTER: Vepa A 

PROVIDER: S-EPMC8296041 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature