Association rules mining for hospital readmission: A case study

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

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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.

Item Type: Article
Uncontrolled Keywords: Apriori algorithm; association rules mining (ARM); hospital readmission
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Computer Science & Information Technology > Department of Artificial Intelligence
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 24 Feb 2022 01:12
Last Modified: 24 Feb 2022 01:12

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