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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Xehetasun bibliografikoak
Egile Nagusiak: Yasser EL-Manzalawy, Mostafa Abbas, Ian Hoaglund, Alvaro Ulloa Cerna, Thomas B. Morland, Christopher M. Haggerty, Eric S. Hall, Brandon K. Fornwalt
Formatua: Artikulua
Hizkuntza:English
Argitaratua: BMC 2021-05-01
Saila:BMC Medical Informatics and Decision Making
Gaiak:
In-hospital mortality prediction
Point-based severity scores
Critical care outcomes
Supervised machine learning
Sarrera elektronikoa:https://doi.org/10.1186/s12911-021-01517-7

Internet

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

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