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...
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2013
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/10356/53033 |
| _version_ | 1826109842149867520 |
|---|---|
| author | Wong, Yong Sheng. |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Wong, Yong Sheng. |
| author_sort | Wong, Yong Sheng. |
| collection | NTU |
| 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. |
| first_indexed | 2024-10-01T02:24:42Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/53033 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:24:42Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-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 |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering Wong, Yong Sheng. Machine learning algorithms for bed management |
| title | 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_short | Machine learning algorithms for bed management |
| title_sort | machine learning algorithms for bed management |
| topic | DRNTU::Engineering::Electrical and electronic engineering |
| url | http://hdl.handle.net/10356/53033 |
| work_keys_str_mv | AT wongyongsheng machinelearningalgorithmsforbedmanagement |