Machine Learning for Benchmarking Critical Care Outcomes

Machine Learning for Benchmarking Critical Care Outcomes

Objectives Enhancing critical care efficacy involves evaluating and improving system functioning. Benchmarking, a retrospective comparison of results against standards, aids risk-adjusted assessment and helps healthcare providers identify areas for improvement based on observed and predicted outcome...

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Bibliographic Details
Main Authors: Louis Atallah, Mohsen Nabian, Ludmila Brochini, Pamela J. Amelung
Format: Article
Language:English
Published: The Korean Society of Medical Informatics 2023-10-01
Series:Healthcare Informatics Research
Subjects:
benchmarking
critical care
length of stay
machine learning
mortality
ventilation
Online Access:http://e-hir.org/upload/pdf/hir-2023-29-4-301.pdf

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