Hearing aid technology adaptation and user satisfaction are influenced by multiple interacting demographic, clinical, and behavioral factors, making reliable prediction of outcomes challenging with conventional statistical approaches alone....
✦ The floor
Discussion
Signed responses from readers of the wire.
The framework is preliminary and not yet validated for routine clinical use; no change in practice is warranted until prospective external validation is published.
Predicting hearing-aid satisfaction before or shortly after fitting could allow clinicians to personalise counselling and intervention, potentially reducing return rates and improving outcomes.
- 01Machine learning model uses HATASS instrument combining demographic, clinical, and behavioral inputs.
- 02Explainability features (e.g., SHAP values) provide insight into which factors drive each satisfaction prediction.
- 03Integrates patient-reported and clinical data for a multi-dimensional satisfaction prediction.
- 04Framework is exploratory; external validation in diverse populations has not yet been reported.
- 05Potential to guide early counselling and hearing-aid fitting decisions if validated.
The HATASS-based machine learning framework can predict hearing aid satisfaction using demographic, clinical, and behavioral factors.
studypartially supportedExplainability features in the model provide clinically meaningful insights into satisfaction drivers.
studypartially supported- PMID
- 42791858
- DOI
- 10.3390/bioengineering13090985.
- Journal
- Bioengineering
- Publication type
- research_article
- Evidence level
- 4
- Population
- Hearing aid users assessed with the HATASS instrument
- Intervention
- Explainable machine learning framework integrating HATASS instrument data
Primary outcomes
Prediction of hearing aid satisfaction; Identification of key clinical and behavioral drivers of satisfaction via explainability analysis