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Clustering of a Health Dataset Using Diagnosis Co-Occurrences

Authors :
Wartelle, Adrien
Mourad-Chehade, Farah
Yalaoui, Farouk
Chrusciel, Jan
Laplanche, David
Sanchez, Stéphane
Laboratoire Informatique et Société Numérique (LIST3N)
Université de Technologie de Troyes (UTT)
Centre hospitalier de Troyes
Laboratoire d'Optimisation des Systèmes Industriels (LOSI)
Université de Technologie de Troyes (UTT)-Université de Technologie de Troyes (UTT)
Source :
Applied Sciences, Applied Sciences, MDPI, 2021, 11 (5), pp.2373. ⟨10.3390/app11052373⟩, Applied Sciences, Vol 11, Iss 2373, p 2373 (2021), Applied Sciences, 2021, 11 (5), pp.2373. ⟨10.3390/app11052373⟩
Publication Year :
2021
Publisher :
HAL CCSD, 2021.

Abstract

Assessing the health profiles of populations is a crucial task to create a coherent healthcare offer. Emergency Departments (EDs) are at the core of the healthcare system and could benefit from this evaluation via an improved understanding of the healthcare needs of their population. This paper proposes a novel hierarchical agglomerative clustering algorithm based on multimorbidity analysis. The proposed approach constructs the clustering dendrogram by introducing new quality indicators based on the relative risk of co-occurrences of patient diagnoses. This algorithm enables the detection of multimorbidity patterns by merging similar patient profiles according to their common diagnoses. The multimorbidity approach has been applied to the data of the largest ED of the Aube Department (Eastern France) to cluster its patient visits. Among the 120,718 visits identified during a 24-month period, 16 clusters were identified, accounting for 94.8% of the visits, with the five most prevalent clusters representing 63.0% of them. The new quality indicators show a coherent and good clustering solution with a cluster membership of 1.81 based on a cluster compactness of 1.40 and a cluster separation of 0.77. Compared to the literature, the proposed approach is appropriate for the discovery of multimorbidity patterns and could help to develop better clustering algorithms for more diverse healthcare datasets.

Details

Language :
English
ISSN :
20763417
Database :
OpenAIRE
Journal :
Applied Sciences, Applied Sciences, MDPI, 2021, 11 (5), pp.2373. ⟨10.3390/app11052373⟩, Applied Sciences, Vol 11, Iss 2373, p 2373 (2021), Applied Sciences, 2021, 11 (5), pp.2373. ⟨10.3390/app11052373⟩
Accession number :
edsair.dedup.wf.001..b510e9abee6f73d76b358121941f4324
Full Text :
https://doi.org/10.3390/app11052373⟩