An Ontology-Based Expert System Approach for Hearing Aid Fitting in a Chaotic Environment

An Ontology-Based Expert System Approach for Hearing Aid Fitting in a Chaotic Environment

Blog Article

Background/Objectives: Hearing aid fitting is critical for Coffee Machines hearing loss rehabilitation but involves complex, interdependent parameters, while AI-based technologies offer promise, their reliance on large datasets and cloud infrastructure limits their use in low-resource settings.In such cases, expert knowledge, manufacturer guidelines, and research findings become the primary sources of information.This study introduces DHAFES (Dynamic Hearing Aid Fitting Expert System), a personalized, ontology-based system for hearing aid fitting.

Methods: A dataset of common patient complaints was analyzed to identify typical auditory issues.A multilingual self-assessment questionnaire was developed to efficiently collect user-reported complaints.With expert input, complaints were categorized 1188 and mapped to corresponding hearing aid solutions.

An ontology, the Hearing Aid Fitting Ontology (HAFO), was developed using OWL 2.DHAFES, a decision support system, was then implemented to process inputs and generate fitting recommendations.Results: DHAFES supports 33 core complaint classes and ensures transparency and traceability.

It operates offline and remotely, improving accessibility in resource-limited environments.Conclusions: DHAFES is a scalable, explainable, and clinically relevant solution for hearing aid fitting.Its ontology-based design enables adaptation to diverse clinical contexts and provides a foundation for future AI integration.