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

Elbattah, Mahmoud; Molloy, Owen

Authors

Mahmoud Elbattah

Owen Molloy



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.

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