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Clustering of a health dataset using diagnosis co-occurrences

Applied Sciences (Switzerland), ISSN: 2076-3417, Vol: 11, Issue: 5, Page: 1-20
2021
  • 14
    Citations
  • 0
    Usage
  • 32
    Captures
  • 1
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    14
    • Citation Indexes
      14
  • Captures
    32
  • Mentions
    1
    • Blog Mentions
      1
      • Blog
        1

Article Description

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 com-mon 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 appro-priate for the discovery of multimorbidity patterns and could help to develop better clustering algorithms for more diverse healthcare datasets.

Bibliographic Details

Adrien Wartelle; Farah Mourad-Chehade; Farouk Yalaoui; Jan Chrusciel; David Laplanche; Stéphane Sanchez

MDPI AG

Materials Science; Physics and Astronomy; Engineering; Chemical Engineering; Computer Science

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