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

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 illnes...

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Bibliographic Details
Main Authors: Yasser EL-Manzalawy, Mostafa Abbas, Ian Hoaglund, Alvaro Ulloa Cerna, Thomas B. Morland, Christopher M. Haggerty, Eric S. Hall, Brandon K. Fornwalt
Format: Article
Language:English
Published: BMC 2021-05-01
Series:BMC Medical Informatics and Decision Making
Subjects:
In-hospital mortality prediction
Point-based severity scores
Critical care outcomes
Supervised machine learning
Online Access:https://doi.org/10.1186/s12911-021-01517-7

Internet

https://doi.org/10.1186/s12911-021-01517-7

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