An ML prediction model based on clinical parameters and automated CT scan features for COVID-19 patients.

ABSTRACT: Outcome prediction for individual patient groups is of paramount importance in terms of selection of appropriate therapeutic options, risk communication to patients and families, and allocating resource through optimum triage. This has become even more necessary in the context of the current COVID-19 pandemic. Widening the spectrum of predictor variables by including radiological parameters alongside the usually utilized demographic, clinical and biochemical ones can facilitate building a comprehensive prediction model. Automation has the potential to build such models with applications to time-critical environments so that a clinician will be able to utilize the model outcomes in real-time decision making at bedside. We show that amalgamation of computed tomogram (CT) data with clinical p

SUBMITTER: Sinha A 

PROVIDER: S-EPMC9252998 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature