Summary of Predicting and Explaining Hearing Aid Usage Using Encoder-decoder with Attention Mechanism and Shap, by Qiqi Su and Eleftheria Iliadou
Predicting and Explaining Hearing Aid Usage Using Encoder-Decoder with Attention Mechanism and SHAP
by Qiqi Su, Eleftheria Iliadou
First submitted to arxiv on: 18 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed novel framework utilizes an Encoder-decoder with attention mechanism (attn-ED) and SHAP to predict future hearing aid usage, thereby improving patient satisfaction and quality of life while reducing societal and financial burdens. The attn-ED model demonstrates strong performance in predicting future hearing aid usage, and SHAP is used to calculate the contribution of various factors affecting this prediction. This framework aims to establish AI models’ credibility in the medical domain through the application of XAI methods. Additionally, it assists clinicians in determining intervention nature. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand what makes people happy with their hearing aids and how we can make them even better. The researchers created a new way to predict how often someone will use their hearing aid based on different factors like personal habits, environment, and more. They tested this method and found it works well. This new tool can also help doctors figure out the best ways to help people with hearing loss. |
Keywords
» Artificial intelligence » Attention » Encoder decoder
