Predicting Hearing Aid Outcomes Using Machine Learning
Big data analysis is a new relevant method to evaluate predictive factors for HA outcomes. It demonstrates HA efficiency to improve intelligibility in quiet and noise and shows the impact of hearing care professionals in maximizing patient's outcome through the selection of the most appropriate technology, fitting parameters, and a regular follow-up ensuring a high daily usage. However, global results must be interpreted with caution on such a heterogeneous population. They would need to be refined by an approach using clusters of patients with similar audiological profiles. .
Authors
- Eric Bailly-Masson
- Catherine Boiteux
- Philippe Roger (ORCID: https://orcid.org/0000-0001-9811-4580)
- Sandrine Mouysset (ORCID: https://orcid.org/0000-0003-0407-0527)
- Bernard Fraysse (ORCID: https://orcid.org/0000-0002-7254-4811)
- Thomas Lespargot
- Fabien Auberger
Institutions
- Université Toulouse III - Paul Sabatier (FR)
- École Polytechnique (FR)
- Université Fédérale de Toulouse Midi-Pyrénées (FR)
- Université Toulouse-I-Capitole (FR)
- Centre Technique Des Industries De La Fonderie (FR)
- Département Santé Animale (FR)
- Institut de Recherche en Informatique de Toulouse (FR)
- Université Toulouse - Jean Jaurès (FR)
- Institut Polytechnique de Bordeaux (FR)
Publication Details
- Journal
- Audiology and Neurotology
- Published
- 2025-02-03
- DOI
- https://doi.org/10.1159/000543916
- Citations
- 3
- Primary Topic
- Hearing Loss and Rehabilitation
- Type
- article
- Field-Weighted Citation Impact
- 4.29