Comparing Clustering Methods Applied to Tinnitus within a Bootstrapped and Diagnostic-Driven Semi-Supervised Framework

Author:

Guillard Robin12ORCID,Hessas Adam2,Korczowski Louis2,Londero Alain3,Congedo Marco1,Loche Vincent4

Affiliation:

1. CNRS, Grenoble INP, GIPSA-Lab, University Grenoble Alpes, 38000 Grenoble, France

2. Siopi, 15 rue des Halles, 75001 Paris, France

3. Service ORL et CCF, Hôpital Européen G.-Pompidou, AP-HP, 20, rue Leblanc, 75015 Paris, France

4. Service d’ORL, Hôpital Claude Huriez, CHU Lille 59000, France

Abstract

The understanding of tinnitus has always been elusive and is largely prevented by its intrinsic heterogeneity. To address this issue, scientific research has aimed at defining stable and easily identifiable subphenotypes of tinnitus. This would allow better disentangling the multiple underlying pathophysiological mechanisms of tinnitus. In this study, three-dimensionality reduction techniques and two clustering methods were benchmarked on a database of 2772 tinnitus patients in order to obtain a reliable segmentation of subphenotypes. In this database, tinnitus patients’ endotypes (i.e., parts of a population with a condition with distinct underlying mechanisms) are reported when diagnosed by an ENT expert in tinnitus management. This partial labeling of the dataset enabled the design of an original semi-supervised framework. The objective was to perform a benchmark of different clustering methods to get as close as possible to the initial ENT expert endotypes. To do so, two metrics were used: a primary one, the quality of the separation of the endotypes already identified in the database, as well as a secondary one, the stability of the obtained clusterings. The relevance of the results was finally reviewed by two ENT experts in tinnitus management. A 20-cluster clustering was selected as the best-performing, the most-clinically relevant, and the most-stable through bootstrapping. This clustering used a T-SNE method as the dimensionality reduction technique and a k-means algorithm as the clustering method. The characteristics of this clustering are presented in this article.

Funder

Felicia and Jean-Jacques Lopez-Loreta Foundation

BPI France bourse French Tech Emergence

Publisher

MDPI AG

Subject

General Neuroscience

Reference70 articles.

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