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Semantic-enhanced models to support timely admission prediction at emergency departments
Journal article   Open access   Peer reviewed

Semantic-enhanced models to support timely admission prediction at emergency departments

Jiexun Li, Lifan Guo, Neal Handly, Aline A. Mai and David A. Thompson
Network modeling and analysis in health informatics and bioinformatics (Wien), v 1(4), pp 161-172
01 Dec 2012
DOI: https://doi.org/10.1007/s13721-012-0014-6
Featured in Collection :   UN Sustainable Development Goals @ Drexel
url
https://doi.org/10.1007/s13721-012-0014-6View
Published, Version of Record (VoR)CC BY V4.0,  Open

Abstract

Life Sciences & Biomedicine Mathematical & Computational Biology Science & Technology
With the rapid outstripping of limited health care resources by the demands on hospital care, it is of critical importance to find more effective and efficient methods of managing care. Our research addresses the problem of emergency department (ED) crowding by building classification models using various types of pre-admission information to help predict the hospital admission of individual patients. We have developed a framework of hospital admission prediction and proposed two novel approaches that capture semantic information in chief complaints to enhance prediction. Our experiments on an ED data set demonstrate that our proposed models outperformed several benchmark methods for admission prediction. These models can potentially be used as decision support tools at hospitals to improve ED throughput rate and enhance patient care.

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3 citations in Web of Science
2 citations in Scopus

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UN Sustainable Development Goals (SDGs)

This publication has contributed to the advancement of the following goals:

#3 Good Health and Well-Being

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Collaboration types
Domestic collaboration
Web of Science research areas
Mathematical & Computational Biology
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