Why the study?
Understanding relationships between hospital readmission factors and readmission length is challenging despite readmissions incurring major healthcare costs.
Association rule mining using the Apriori algorithm can identify hidden patterns between patient demographics and readmission length to help minimize hospital readmissions.
May identify demographic-readmission patterns via data mining; hypothesis-generating and should not yet change practice.
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.
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Miswan et al. (2021) studied this question.
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