Esnault C., Rollot M., Guilmin P., Zucker Jean-Daniel. (2023). Qluster : an easy-to-implement generic workflow for robust clustering of health data. Frontiers in Artificial Intelligence, 5, p. 1055294 [20 p.].
Titre du document
Qluster : an easy-to-implement generic workflow for robust clustering of health data
Esnault C., Rollot M., Guilmin P., Zucker Jean-Daniel
Source
Frontiers in Artificial Intelligence, 2023,
5, p. 1055294 [20 p.]
The exploration of heath data by clustering algorithms allows to better describe the populations of interest by seeking the sub-profiles that compose it. This therefore reinforces medical knowledge, whether it is about a disease or a targeted population in real life. Nevertheless, contrary to the so-called conventional biostatistical methods where numerous guidelines exist, the standardization of data science approaches in clinical research remains a little discussed subject. This results in a significant variability in the execution of data science projects, whether in terms of algorithms used, reliability and credibility of the designed approach. Taking the path of parsimonious and judicious choice of both algorithms and implementations at each stage, this article proposes Qluster, a practical workflow for performing clustering tasks. Indeed, this workflow makes a compromise between (1) genericity of applications (e.g. usable on small or big data, on continuous, categorical or mixed variables, on database of high-dimensionality or not), (2) ease of implementation (need for few packages, few algorithms, few parameters, ...), and (3) robustness (e.g. use of proven algorithms and robust packages, evaluation of the stability of clusters, management of noise and multicollinearity). This workflow can be easily automated and/or routinely applied on a wide range of clustering projects. It can be useful both for data scientists with little experience in the field to make data clustering easier and more robust, and for more experienced data scientists who are looking for a straightforward and reliable solution to routinely perform preliminary data mining. A synthesis of the literature on data clustering as well as the scientific rationale supporting the proposed workflow is also provided. Finally, a detailed application of the workflow on a concrete use case is provided, along with a practical discussion for data scientists. An implementation on the Dataiku platform is available upon request to the authors.
Plan de classement
Sciences fondamentales / Techniques d'analyse et de recherche [020]
;
Santé : généralités [050]
;
Informatique [122]
