research-article

Data-driven patient segmentation using K-means clustering: the case of hip fracture care in Ireland

Authors:

Mahmoud Elbattah and

Owen MolloyAuthors Info & Claims

ACSW '17: Proceedings of the Australasian Computer Science Week Multiconference

January 2017

Article No.: 60, Pages 1 - 8

https://doi.org/10.1145/3014812.3014874

Published: 31 January 2017 Publication History

Abstract

Machine learning continues to forge the future of decision making in a broad diversity of domains including healthcare. Data-driven methods are increasingly geared towards leveraging evidence-based insights from large volumes of patient data. In this context, this paper embraces a mere data-driven approach for the segmentation of patients with application to hip fracture care in Ireland. Using K-Means clustering, elderly patients are grouped based on the similarity of age, length of stay (LOS) and elapsed time to surgery. We utilise a dataset retrieved from the Irish Hip fracture Database (IHFD) covering the period of two years (2013--2014). Our results suggest the presence of three coherent clusters of patients. Through cluster analysis, possible correlations are explored in relation to patient characteristics, care-related factors, and patient outcomes. For instance, the study inspects the potential impact of time to surgery on patient outcomes (e.g. LOS) within the discovered clusters of patients. Furthermore, the clusters are visually interpreted in a demographic context with respect to the structure of the healthcare system in Ireland. Broadly, the study is claimed to serve useful purposes for healthcare executives in Ireland for developing more patient-centred care strategies.

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Cited By

Sheng YBond RJaiswal RDinsmore JDoyle J(2024)Augmenting K-Means Clustering With Qualitative Data to Discover the Engagement Patterns of Older Adults With Multimorbidity When Using Digital Health Technologies: Proof-of-Concept TrialJournal of Medical Internet Research10.2196/4628726(e46287)Online publication date: 28-Mar-2024
https://doi.org/10.2196/46287
Resta EResta OCostantiello ALeogrande A(2024)The hospital emigration to another region in the light of the environmental, social and governance model in Italy during the period 2004-2021BMC Public Health10.1186/s12889-024-19369-x24:1Online publication date: 15-Jul-2024
https://doi.org/10.1186/s12889-024-19369-x
Boussina AWardi GShashikumar SMalhotra AZheng KNemati S(2023)Representation Learning and Spectral Clustering for the Development and External Validation of Dynamic Sepsis Phenotypes: Observational Cohort StudyJournal of Medical Internet Research10.2196/4561425(e45614)Online publication date: 23-Jun-2023
https://doi.org/10.2196/45614
Show More Cited By

Index Terms

Data-driven patient segmentation using K-means clustering: the case of hip fracture care in Ireland
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
2. Mathematics of computing
  1. Probability and statistics
    1. Statistical paradigms
      1. Cluster analysis

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cover image ACM Other conferences

ACSW '17: Proceedings of the Australasian Computer Science Week Multiconference

January 2017

615 pages

ISBN:9781450347686

DOI:10.1145/3014812

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 31 January 2017

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ACSW 2017

ACSW 2017: Australasian Computer Science Week 2017

January 30 - February 3, 2017

Geelong, Australia

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ACSW '17 Paper Acceptance Rate 78 of 156 submissions, 50%;

Overall Acceptance Rate 204 of 424 submissions, 48%

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Cited By

Sheng YBond RJaiswal RDinsmore JDoyle J(2024)Augmenting K-Means Clustering With Qualitative Data to Discover the Engagement Patterns of Older Adults With Multimorbidity When Using Digital Health Technologies: Proof-of-Concept TrialJournal of Medical Internet Research10.2196/4628726(e46287)Online publication date: 28-Mar-2024
https://doi.org/10.2196/46287
Resta EResta OCostantiello ALeogrande A(2024)The hospital emigration to another region in the light of the environmental, social and governance model in Italy during the period 2004-2021BMC Public Health10.1186/s12889-024-19369-x24:1Online publication date: 15-Jul-2024
https://doi.org/10.1186/s12889-024-19369-x
Boussina AWardi GShashikumar SMalhotra AZheng KNemati S(2023)Representation Learning and Spectral Clustering for the Development and External Validation of Dynamic Sepsis Phenotypes: Observational Cohort StudyJournal of Medical Internet Research10.2196/4561425(e45614)Online publication date: 23-Jun-2023
https://doi.org/10.2196/45614
Sandoval Serrano DRincón JMejía-Restrepo JNúñez-Valdez EGarcía-Díaz V(2022)Forecast of Medical Costs in Health Companies Using Models Based on Advanced AnalyticsAlgorithms10.3390/a1504010615:4(106)Online publication date: 23-Mar-2022
https://doi.org/10.3390/a15040106
Beuken MKanera IEzendam NBraun SZoet M(2022)Identification and Potential Use of Clusters of Patients With Colorectal Cancer and Patients With Prostate Cancer in Clinical Practice: Explorative Mixed Methods StudyJMIR Cancer10.2196/429088:4(e42908)Online publication date: 27-Dec-2022
https://doi.org/10.2196/42908
Ekerete IGarcia-Constantino MDiaz-Skeete YNugent CMcLaughlin J(2021)Fusion of Unobtrusive Sensing Solutions for Sprained Ankle Rehabilitation Exercises Monitoring in Home EnvironmentsSensors10.3390/s2122756021:22(7560)Online publication date: 13-Nov-2021
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Maione CLuíza da Costa NBarbosa FBarbosa R(2021)A Cluster Analysis Methodology for the Categorization of Soil Samples for Forensic Sciences Based on Elemental FingerprintApplied Artificial Intelligence10.1080/08839514.2021.201094136:1Online publication date: 20-Dec-2021
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