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Cluster Sampling Method

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...

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

Robin Guillard^1,2, Adam Hessas², Louis Korczowski²

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

|May 16, 2023

Summary

This study identifies distinct tinnitus subphenotypes using advanced clustering techniques. A 20-cluster model, combining T-SNE and k-means, offers the most clinically relevant and stable segmentation for understanding tinnitus heterogeneity.

Keywords:

benchmark bootstrap expert validation semi-supervised clustering subphenotype tinnitus

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Area of Science:

Otorhinolaryngology
Computational Biology
Medical Data Science

Background:

Tinnitus understanding is hindered by its complex heterogeneity.
Identifying stable tinnitus subphenotypes is crucial for elucidating underlying pathophysiological mechanisms.
Existing research lacks reliable methods for tinnitus subtyping.

Purpose of the Study:

To benchmark dimensionality reduction and clustering methods for tinnitus subphenotype segmentation.
To develop and validate a semi-supervised framework for classifying tinnitus endotypes.
To achieve a subphenotype clustering that closely aligns with expert-diagnosed endotypes.

Main Methods:

Benchmarking of three-dimensionality reduction techniques (including T-SNE) and two clustering methods (including k-means).
Application of a novel semi-supervised framework on a database of 2772 tinnitus patients with partial expert-labeled endotypes.
Evaluation using primary (endotype separation quality) and secondary (clustering stability) metrics, reviewed by ENT experts.

Main Results:

A 20-cluster segmentation using T-SNE and k-means was identified as the optimal approach.
This clustering demonstrated superior performance, clinical relevance, and stability through bootstrapping.
The characteristics of this clinically relevant tinnitus subphenotype segmentation are detailed.

Conclusions:

The developed semi-supervised framework effectively segments tinnitus patients into clinically relevant subphenotypes.
The T-SNE and k-means based 20-cluster model provides a stable and reliable method for tinnitus classification.
This approach advances the understanding and potential management of tinnitus heterogeneity.