OASIS +: leveraging machine learning to improve the prognostic accuracy of OASIS severity score for predicting in-hospital mortality.

ABSTRACT:

Background

Severity scores assess the acuity of critical illness by penalizing for the deviation of physiologic measurements from normal and aggregating these penalties (also called "weights" or "subscores") into a final score (or probability) for quantifying the severity of critical illness (or the likelihood of in-hospital mortality). Although these simple additive models are human readable and interpretable, their predictive performance needs to be further improved.

Methods

We present OASIS +, a variant of the Oxford Acute Severity of Illness Score (OASIS) in which an ensemble of 200 decision trees is used to predict in-hospital mortality based on the 10 same clinical variables in OASIS.

Results

Using a test set of 9566 admissions extracted from the MIMIC-III datab

SUBMITTER: El-Manzalawy Y 

PROVIDER: S-EPMC8118103 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature