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

Abstract

Understanding the factors that contribute to optimal hearing aid fitting and hearing aid user experiences is crucial in order to increase the satisfaction and quality of life of hearing loss patients, 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.

Extension

Using Attn-ED with three XAI methods: SHAP, LIME, and DiCe is in this GitHub Repo.

Bibtex

@inproceedings{su2022predicting,
  title={Predicting and Explaining Hearing Aid Usage Using Encoder-Decoder with Attention Mechanism and SHAP},
  author={Su, Qiqi and Iliadou, Eleftheria},
  booktitle={2022 16th International Conference on Signal-Image Technology \& Internet-Based Systems (SITIS)},
  pages={308--315},
  year={2022},
  organization={IEEE}
}

Recommended citation: Su, Q. and Iliadou, E.(2022). "Predicting and Explaining Hearing Aid Usage Using Encoder-Decoder with Attention Mechanism and SHAP." In 2022 16th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) (pp. 308-315). IEEE Computer Society. https://ieeexplore.ieee.org/abstract/document/10090071