Multivariable mortality risk prediction using machine learning for COVID-19 patients at admission (AICOVID).

ABSTRACT: In Coronavirus disease 2019 (COVID-19), early identification of patients with a high risk of mortality can significantly improve triage, bed allocation, timely management, and possibly, outcome. The study objective is to develop and validate individualized mortality risk scores based on the anonymized clinical and laboratory data at admission and determine the probability of Deaths at 7 and 28 days. Data of 1393 admitted patients (Expired-8.54%) was collected from six Apollo Hospital centers (from April to July 2020) using a standardized template and electronic medical records. 63 Clinical and Laboratory parameters were studied based on the patient's initial clinical state at admission and laboratory parameters within the first 24 h. The Machine Learning (ML) modelling was performed using

SUBMITTER: Kar S 

PROVIDER: S-EPMC8211710 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature