Deeper insight into speech characteristics of patients at ultra-high risk using classification and explainability models
The findings in this study suggest that weak coherence play a key role in classification. No significant differences in the use of "I" and filled pauses were found between groups using a statistical test, but an explainability model showed its different contributions. The contribution of each linguistic feature to the classification by patient group provided deeper insight into linguistic manifestations of each patient group and their subtle differences, which could help better analyze and understand patients' language behaviors.
Authors
- Marie‐Odile Krebs (ORCID: https://orcid.org/0000-0002-4715-9890)
- Michel Walter (ORCID: https://orcid.org/0000-0002-3071-7673)
- Philippe Lenca (ORCID: https://orcid.org/0000-0001-5014-2347)
- Yannis Haralambous (ORCID: https://orcid.org/0000-0003-1443-6115)
- Deok-Hee Kim-Dufor (ORCID: https://orcid.org/0000-0002-0411-0093)
- Christophe Lemey (ORCID: https://orcid.org/0000-0002-7308-7958)
Institutions
- Centre National de la Recherche Scientifique (FR)
- Inserm (FR)
- Université de Bretagne Occidentale (FR)
- Université Sorbonne Nouvelle (FR)
- Institut Mines-Télécom (FR)
- Sorbonne Université (FR)
- Sorbonne Paris Cité (FR)
- Laboratoire des Sciences et Techniques de l’Information de la Communication et de la Connaissance (FR)
- Centre Hospitalier Sainte-Anne (FR)
- IMT Atlantique (FR)
- Centre Hospitalier Régional Universitaire de Brest (FR)
- Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances en e-Santé
- Université Paris 1 Panthéon-Sorbonne (FR)
Publication Details
- Journal
- Frontiers in Psychiatry
- Published
- 2025-06-16
- DOI
- https://doi.org/10.3389/fpsyt.2025.1595197
- Citations
- 4
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 9.92