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

Authors: Mahmoud Elbattah; Owen Molloy

Addresses: Laboratoire MIS, Université de Picardie Jules Verne, 80080 Amiens, France ' College of Engineering and Informatics, National University of Ireland Galway, H91 TK33 Galway City, Ireland

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.

Keywords: data mining; data analytics; machine learning; unsupervised learning; clustering; cluster analysis; rule mining; hip fracture care; Irish hip fracture database; elderly healthcare.

DOI: 10.1504/IJDS.2020.115875

International Journal of Data Science, 2020 Vol.5 No.4, pp.290 - 315

Received: 12 Jun 2020
Accepted: 05 Mar 2021
Published online: 25 Jun 2021 *

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