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.

通过使用具有注意机制的编码器 - 解码器模型（attn-ED）预测未来助听器的使用情况，并利用 SHAP 解释影响助听器使用情况的因素，本研究提出一种新的框架，旨在改善听力丧失患者的满意度和生活质量，减少社会和经济负担，并为 AI 模型在医疗领域的使用提供可信度，同时还可协助临床医生确定干预方式。