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Multi-view Clustering of Heterogeneous Health Data: Application to Systemic Sclerosis

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Parallel Problem Solving from Nature – PPSN XVII (PPSN 2022)

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Abstract

Electronic health records (EHRs) involve heterogeneous data types such as binary, numeric and categorical attributes. As traditional clustering approaches require the definition of a single proximity measure, different data types are typically transformed into a common format or amalgamated through a single distance function. Unfortunately, this early transformation step largely pre-determines the cluster analysis results and can cause information loss, as the relative importance of different attributes is not considered. This exploratory work aims to avoid this premature integration of attribute types prior to cluster analysis through a multi-objective evolutionary algorithm called MVMC. This approach allows multiple data types to be integrated into the clustering process, explore trade-offs between them, and determine consensus clusters that are supported across these data views. We evaluate our approach in a case study focusing on systemic sclerosis (SSc), a highly heterogeneous auto-immune disease that can be considered a representative example of an EHRs data problem. Our results highlight the potential benefits of multi-view learning in an EHR context. Furthermore, this comprehensive classification integrating multiple and various data sources will help to understand better disease complications and treatment goals.

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Notes

  1. 1.

    SSc patients in the Internal Medicine Department of University Hospital of Lille, France, between October 2014 and December 2021 as part of the FHU PRECISE project (PREcision health in Complex Immune-mediated inflammatory diseaSEs); sample collection and usage authorization, CPP 2019-A01083-54.

  2. 2.

    Note that the Silhouette score is intended to compare different partitions produced by a single method. Usually, the Rand index is preferred to the Silhouette score to compare two solutions when a ground-truth partition is available [35].

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Acknowledgments

The authors are grateful to the University of Lille, CHU Lille, and INSERM, founded by the MEL through the I-Site cluster humAIn@Lille.

Author information

Authors and Affiliations

  1. Univ. Lille, CNRS, Centrale Lille, UMR 9189 CRIStAL, 59000, Lille, France

    Adán José-García, Julie Jacques & Clarisse Dhaenens

  2. FGES, Université Catholique de Lille, 59000, Lille, France

    Julie Jacques

  3. Univ. Lille, Inserm, CHU Lille, U1286, INFINITE, 59000, Lille, France

    Alexandre Filiot & Vincent Sobanski

  4. Univ. Lille, Inserm, CHU Lille, Service de Médecine Interne et Immunologie Clinique, CeRAINO, U1286, INFINITE, 59000, Lille, France

    David Launay

  5. Institut Universitaire de France (IUF), Paris, France

    Vincent Sobanski

  6. Alliance Manchester Business School, University of Manchester, Manchester, UK

    Julia Handl

Authors
  1. Adán José-García
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  2. Julie Jacques
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  7. Clarisse Dhaenens
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Corresponding author

Correspondence to Adán José-García .

Editor information

Editors and Affiliations

  1. TU Dortmund, Dortmund, Germany

    Günter Rudolph

  2. Leiden University, Leiden, The Netherlands

    Anna V. Kononova

  3. Shinshu University, Nagano, Japan

    Hernán Aguirre

  4. Technische Universität Dresden, Dresden, Germany

    Pascal Kerschke

  5. University of Stirling, Stirling, UK

    Gabriela Ochoa

  6. Jožef Stefan Institute, Ljubljana, Slovenia

    Tea Tušar

Appendix

Appendix

This Appendix includes figures complementing the results of the experiments presented in Sect. 5. From Fig. 5 (Appendix), it is clear that the determined number of clusters is three as the Silhouette index obtained its highest point value at this point, \(k=3\). Also, from the Pareto front approximations obtained by these configurations, a substantial inference of the {Num} view is observed over the {Bin} and {Gower} views, respectively. Accordingly, the clustering solutions and the weighted embedding space are remarkably similar between these two data-view configurations.

Fig. 5.
figure 5

MVMC clustering solutions for two data-view configurations, {Bin,Num} and {Num,Gower}. Each configuration includes (i) the convergence plots shown in blue and gray, with the best solution marked red; (ii) the Pareto front approximation corresponding to the estimated k value; (iii) the clustering solution, which is visualized in a weighted embedding space associated with the data views in the configuration. (Color figure online)

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José-García, A. et al. (2022). Multi-view Clustering of Heterogeneous Health Data: Application to Systemic Sclerosis. In: Rudolph, G., Kononova, A.V., Aguirre, H., Kerschke, P., Ochoa, G., Tušar, T. (eds) Parallel Problem Solving from Nature – PPSN XVII. PPSN 2022. Lecture Notes in Computer Science, vol 13399. Springer, Cham. https://doi.org/10.1007/978-3-031-14721-0_25

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  • DOI: https://doi.org/10.1007/978-3-031-14721-0_25

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