Please use this identifier to cite or link to this item: https://ahro.austin.org.au/austinjspui/handle/1/31964
Title: Early prediction of hospital admission of emergency department patients.
Austin Authors: Kishore, Kartik ;Braitberg, George;Holmes, Natasha E ;Bellomo, Rinaldo 
Affiliation: Data Analytics Research and Evaluation (DARE) Centre
Emergency
Issue Date: Aug-2023
Date: 2023
Publication information: Emergency medicine Australasia: EMA 2023-08; 35(4)
Abstract: The early prediction of hospital admission is important to ED patient management. Using available electronic data, we aimed to develop a predictive model for hospital admission.
URI: https://ahro.austin.org.au/austinjspui/handle/1/31964
DOI: 10.1111/1742-6723.14169
ORCID: 0000-0002-4013-3364
0000-0001-8501-4054
0000-0002-1650-8939
Journal: Emergency Medicine Australasia : EMA
PubMed URL: 36634916
ISSN: 1742-6723
Type: Journal Article
Subjects: NEAT
SHAP
Admission prediction
Machine learning
Appears in Collections:Journal articles

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