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Association rules mining for hospital readmission: A case study

As an indicator of healthcare quality and performance, hospital readmission incurs major costs for healthcare systems worldwide. Understanding the relationships between readmission factors, such as input features and readmission length, is challenging following intricate hospital readmission procedu...

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Main Authors: Miswan, Nor Hamizah, Sulaiman, `Ismat Mohd, Chan, Chee Seng, Ng, Chong Guan
Format: Article
Published: MDPI 2021
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.um.edu.my/26364/
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spelling my.um.eprints.263642022-02-24T01:12:25Z http://eprints.um.edu.my/26364/ Association rules mining for hospital readmission: A case study Miswan, Nor Hamizah Sulaiman, `Ismat Mohd Chan, Chee Seng Ng, Chong Guan QA75 Electronic computers. Computer science As an indicator of healthcare quality and performance, hospital readmission incurs major costs for healthcare systems worldwide. Understanding the relationships between readmission factors, such as input features and readmission length, is challenging following intricate hospital readmission procedures. This study discovered the significant correlation between potential readmission factors (threshold of various settings for readmission length) and basic demographic variables. Association rule mining (ARM), particularly the Apriori algorithm, was utilised to extract the hidden input variable patterns and relationships among admitted patients by generating supervised learning rules. The mined rules were categorised into two outcomes to comprehend readmission data; (i) the rules associated with various readmission length and (ii) several expert-validated variables related to basic demographics (gender, race, and age group). The extracted rules proved useful to facilitate decision-making and resource preparation to minimise patient readmission. MDPI 2021-11 Article PeerReviewed Miswan, Nor Hamizah and Sulaiman, `Ismat Mohd and Chan, Chee Seng and Ng, Chong Guan (2021) Association rules mining for hospital readmission: A case study. Mathematics, 9 (21). ISSN 2227-7390, DOI https://doi.org/10.3390/math9212706 <https://doi.org/10.3390/math9212706>. 10.3390/math9212706
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Miswan, Nor Hamizah
Sulaiman, `Ismat Mohd
Chan, Chee Seng
Ng, Chong Guan
Association rules mining for hospital readmission: A case study
description As an indicator of healthcare quality and performance, hospital readmission incurs major costs for healthcare systems worldwide. Understanding the relationships between readmission factors, such as input features and readmission length, is challenging following intricate hospital readmission procedures. This study discovered the significant correlation between potential readmission factors (threshold of various settings for readmission length) and basic demographic variables. Association rule mining (ARM), particularly the Apriori algorithm, was utilised to extract the hidden input variable patterns and relationships among admitted patients by generating supervised learning rules. The mined rules were categorised into two outcomes to comprehend readmission data; (i) the rules associated with various readmission length and (ii) several expert-validated variables related to basic demographics (gender, race, and age group). The extracted rules proved useful to facilitate decision-making and resource preparation to minimise patient readmission.
format Article
author Miswan, Nor Hamizah
Sulaiman, `Ismat Mohd
Chan, Chee Seng
Ng, Chong Guan
author_facet Miswan, Nor Hamizah
Sulaiman, `Ismat Mohd
Chan, Chee Seng
Ng, Chong Guan
author_sort Miswan, Nor Hamizah
title Association rules mining for hospital readmission: A case study
title_short Association rules mining for hospital readmission: A case study
title_full Association rules mining for hospital readmission: A case study
title_fullStr Association rules mining for hospital readmission: A case study
title_full_unstemmed Association rules mining for hospital readmission: A case study
title_sort association rules mining for hospital readmission: a case study
publisher MDPI
publishDate 2021
url http://eprints.um.edu.my/26364/
_version_ 1735409402609926144
score 13.211869

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