Predicting and Explaining Hearing Aid Usage Using Encoder-Decoder with Attention Mechanism and SHAP

TL;DR

This paper introduces a novel AI framework combining encoder-decoder with attention and SHAP for predicting and explaining hearing aid usage, aiming to enhance patient satisfaction and support clinical decisions.

Contribution

It presents a new predictive model with explainability for hearing aid usage, integrating attention mechanisms and SHAP to identify contributing factors.

Findings

Attn-ED achieves high accuracy in predicting hearing aid usage.

SHAP effectively identifies key factors influencing usage.

Framework supports clinical decision-making and personalized interventions.

Abstract

It is essential to understand the personal, behavioral, environmental, and other factors that correlate with optimal hearing aid fitting and hearing aid users' experiences in order to improve hearing loss patient satisfaction and quality of life, as well as reduce societal and financial burdens. This work proposes a novel framework that uses Encoder-decoder with attention mechanism (attn-ED) for predicting future hearing aid usage and SHAP to explain the factors contributing to this prediction. It has been demonstrated in experiments that attn-ED performs well at predicting future hearing aid usage, and that SHAP can be utilized to calculate the contribution of different factors affecting hearing aid usage. This framework aims to establish confidence that AI models can be utilized in the medical domain with the use of XAI methods. Moreover, the proposed framework can also assist clinicians in determining the nature of interventions.