Mining the Irish hip fracture database: Learning factors contributing to care outcomes

Elbattah, Mahmoud; Molloy, Owen

Abstract

Data analytics has opened the door for improving many aspects pertaining to the delivery of healthcare. This study avails of unsupervised machine learning to extract knowledge from the Irish hip fracture database (IHFD). The dataset under consideration contained patient records over three years 2013–2015. The process of knowledge discovery included using data clustering and rule mining. With cluster analysis, possible correlations were explored related to patient characteristics, care-related factors or outcomes. Further, association rules were discovered to learn the potential factors leading to a prolonged length of stay (LOS). In essence, our results highlight the significant impact of the pre-surgery waiting time on the LOS. The cluster analysis and association rules consistently emphasised that patients who experienced longer periods of pre-surgery waiting time tended to have longer LOS periods. The insights delivered are believed to yield practical implications to be considered within the treatment of hip fractures, especially in the case of elderly patients.

Citation

Elbattah, M., & Molloy, O. (2020). Mining the Irish hip fracture database: Learning factors contributing to care outcomes. International Journal of Data Science, 5(4), 290. https://doi.org/10.1504/ijds.2020.115875

• Journal Article Type: Article
• Acceptance Date: Mar 6, 2020
• Online Publication Date: Jun 18, 2021
• Publication Date: 2020
• Deposit Date: Apr 26, 2022
• Journal: International Journal of Data Science
• Print ISSN: 2053-0811
• Electronic ISSN: 2053-082X
• Publisher: Inderscience
• Peer Reviewed: Peer Reviewed
• Volume: 5
• Issue: 4
• Pages: 290
• DOI: https://doi.org/10.1504/ijds.2020.115875
• Keywords: Metals and Alloys; Mechanical Engineering; Mechanics of Materials
• Public URL: https://uwe-repository.worktribe.com/output/9206307