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

• URL: http://arxiv.org/abs/2405.11275v1
• Date: Sat, 18 May 2024 12:19:16 GMT
• Title: Predicting and Explaining Hearing Aid Usage Using Encoder-Decoder with Attention Mechanism and SHAP
• Authors: Qiqi Su, Eleftheria Iliadou,
• Abstract summary: It is essential to understand the personal, behavioral, environmental, and other factors that correlate with optimal hearing aid fitting and hearing aid users' experiences. It has been demonstrated in experiments that attn-ED performs well at predicting future hearing aid usage. The proposed framework can also assist clinicians in determining the nature of interventions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• NeuroSpex: Neuro-Guided Speaker Extraction with Cross-Modal Attention [47.8479647938849]
  We present a neuro-guided speaker extraction model, i.e. NeuroSpex, using the EEG response of the listener as the sole auxiliary reference cue. We propose a novel EEG signal encoder that captures the attention information. Additionally, we propose a cross-attention (CA) mechanism to enhance the speech feature representations.
  arXiv  Detail & Related papers  (2024-09-04T07:33:01Z)
• Informing clinical assessment by contextualizing post-hoc explanations of risk prediction models in type-2 diabetes [50.8044927215346]
  We consider a comorbidity risk prediction scenario and focus on contexts regarding the patients clinical state. We employ several state-of-the-art LLMs to present contexts around risk prediction model inferences and evaluate their acceptability. Our paper is one of the first end-to-end analyses identifying the feasibility and benefits of contextual explanations in a real-world clinical use case.
  arXiv  Detail & Related papers  (2023-02-11T18:07:11Z)
• Actionable Recourse via GANs for Mobile Health [0.0]
  Recourse via counterfactuals provides tangible mechanisms to modify user predictions. We demonstrate the feasibility of GAN-generated recourse for mobile health applications on ensemble-survival-analysis-based prediction of medium-term engagement in the Safe Delivery App.
  arXiv  Detail & Related papers  (2022-11-12T00:25:43Z)
• Inference and Denoise: Causal Inference-based Neural Speech Enhancement [83.4641575757706]
  This study addresses the speech enhancement (SE) task within the causal inference paradigm by modeling the noise presence as an intervention. The proposed causal inference-based speech enhancement (CISE) separates clean and noisy frames in an intervened noisy speech using a noise detector and assigns both sets of frames to two mask-based enhancement modules (EMs) to perform noise-conditional SE.
  arXiv  Detail & Related papers  (2022-11-02T15:03:50Z)
• Can Current Explainability Help Provide References in Clinical Notes to Support Humans Annotate Medical Codes? [53.45585591262433]
  We present an explainable Read, Attend, and Code (xRAC) framework and assess two approaches, attention score-based xRAC-ATTN and model-agnostic knowledge-distillation-based xRAC-KD. We find that the supporting evidence text highlighted by xRAC-ATTN is of higher quality than xRAC-KD whereas xRAC-KD has potential advantages in production deployment scenarios.
  arXiv  Detail & Related papers  (2022-10-28T04:06:07Z)
• Evaluating and Optimizing Hearing-Aid Self-Fitting Methods using Population Coverage [0.4014524824655105]
  Adults with mild-to-moderate hearing loss can use over-the-counter hearing aids to treat their hearing loss at a fraction of traditional hearing care costs. These products incorporate self-fitting methods that allow end-users to configure their hearing aids without the help of an audiologist. This paper considers how to design effective self-fitting methods and whether we may evaluate certain aspects of their design without resorting to expensive user studies.
  arXiv  Detail & Related papers  (2022-10-25T03:02:55Z)
• Deep Feature Learning for Medical Acoustics [78.56998585396421]
  The purpose of this paper is to compare different learnables in medical acoustics tasks. A framework has been implemented to classify human respiratory sounds and heartbeats in two categories, i.e. healthy or affected by pathologies.
  arXiv  Detail & Related papers  (2022-08-05T10:39:37Z)
• Personalized and Reliable Decision Sets: Enhancing Interpretability in Clinical Decision Support Systems [0.08594140167290096]
  The system combines a decision set of rules with a machine learning scheme to offer global and local interpretability. The reliability analysis of individual predictions is also addressed, contributing to further personalized interpretability.
  arXiv  Detail & Related papers  (2021-07-15T17:36:24Z)
• DHASP: Differentiable Hearing Aid Speech Processing [23.101074347473904]
  An appropriate amplification fitting for the listener's hearing disability is critical for good performance. In this paper, we introduce an alternative approach to finding the optimal fitting by introducing a hearing aid speech processing framework. The framework is fully differentiable, thus can employ the back-propagation algorithm for efficient, data-driven optimisation. Our initial objective experiments show promising results for noise-free speech amplification, where the automatically optimised processors outperform one of the well recognised hearing aid prescriptions.
  arXiv  Detail & Related papers  (2021-03-15T17:34:22Z)
• Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration [55.88616573143478]
  Outcome prediction from clinical text can prevent doctors from overlooking possible risks. Diagnoses at discharge, procedures performed, in-hospital mortality and length-of-stay prediction are four common outcome prediction targets. We propose clinical outcome pre-training to integrate knowledge about patient outcomes from multiple public sources.
  arXiv  Detail & Related papers  (2021-02-08T10:26:44Z)
• Personalization of Hearing Aid Compression by Human-In-Loop Deep Reinforcement Learning [3.402787708517184]
  Existing prescriptive compression strategies used in hearing aid fitting are designed based on gain averages from a group of users which are not necessarily optimal for a specific user. This paper presents a human-in-loop deep reinforcement learning approach that personalizes hearing aid compression to achieve improved hearing perception.
  arXiv  Detail & Related papers  (2020-07-01T02:50:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.