Machine learning algorithms for bed management

Singapore’s aging population has rapidly increased over the years and the number of patients admitted into local hospitals keeps increasing. Through this, there will be a huge strain on hospital resources. Hospital length of stay (LOS) is used as a key indicator of hospital management because of its...

Full description

Saved in:

Bibliographic Details
Main Author:	Wong, Yong Sheng.
Other Authors:	School of Electrical and Electronic Engineering
Format:	Final Year Project
Language:	English
Published:	2013
Subjects:	DRNTU::Engineering::Electrical and electronic engineering
Online Access:	http://hdl.handle.net/10356/53033
Tags:	Add Tag No Tags, Be the first to tag this record!
Institution:	Nanyang Technological University
Language:	English

id	sg-ntu-dr.10356-53033
record_format	dspace
spelling	sg-ntu-dr.10356-530332023-07-07T16:40:46Z Machine learning algorithms for bed management Wong, Yong Sheng. School of Electrical and Electronic Engineering Justin Dauwels DRNTU::Engineering::Electrical and electronic engineering Singapore’s aging population has rapidly increased over the years and the number of patients admitted into local hospitals keeps increasing. Through this, there will be a huge strain on hospital resources. Hospital length of stay (LOS) is used as a key indicator of hospital management because of its relationship to the amount of resource consumed. By enabling to identify trends of LOS, hospitals are able to better plan resources for future needs. In this report, coxian phase type distribution is used to help analyse the trend of LOS. By fitting the dataset to the distribution model, a growing trend can be discovered in the proportion of patients from a hospital in Singapore. Bachelor of Engineering 2013-05-29T08:04:34Z 2013-05-29T08:04:34Z 2013 2013 Final Year Project (FYP) http://hdl.handle.net/10356/53033 en Nanyang Technological University 53 p. application/pdf
institution	Nanyang Technological University
building	NTU Library
continent	Asia
country	Singapore Singapore
content_provider	NTU Library
collection	DR-NTU
language	English
topic	DRNTU::Engineering::Electrical and electronic engineering
spellingShingle	DRNTU::Engineering::Electrical and electronic engineering Wong, Yong Sheng. Machine learning algorithms for bed management
description	Singapore’s aging population has rapidly increased over the years and the number of patients admitted into local hospitals keeps increasing. Through this, there will be a huge strain on hospital resources. Hospital length of stay (LOS) is used as a key indicator of hospital management because of its relationship to the amount of resource consumed. By enabling to identify trends of LOS, hospitals are able to better plan resources for future needs. In this report, coxian phase type distribution is used to help analyse the trend of LOS. By fitting the dataset to the distribution model, a growing trend can be discovered in the proportion of patients from a hospital in Singapore.
author2	School of Electrical and Electronic Engineering
author_facet	School of Electrical and Electronic Engineering Wong, Yong Sheng.
format	Final Year Project
author	Wong, Yong Sheng.
author_sort	Wong, Yong Sheng.
title	Machine learning algorithms for bed management
title_short	Machine learning algorithms for bed management
title_full	Machine learning algorithms for bed management
title_fullStr	Machine learning algorithms for bed management
title_full_unstemmed	Machine learning algorithms for bed management
title_sort	machine learning algorithms for bed management
publishDate	2013
url	http://hdl.handle.net/10356/53033
_version_	1772825174722215936

Machine learning algorithms for bed management

Similar Items