Predicting Hearing Aid Outcomes Using Machine Learning.

IF 1.6 4区 医学 Q2 AUDIOLOGY & SPEECH-LANGUAGE PATHOLOGY
Pauline Roger, Thomas Lespargot, Catherine Boiteux, Eric Bailly-Masson, Fabien Auberger, Sandrine Mouysset, Bernard Fraysse
{"title":"Predicting Hearing Aid Outcomes Using Machine Learning.","authors":"Pauline Roger, Thomas Lespargot, Catherine Boiteux, Eric Bailly-Masson, Fabien Auberger, Sandrine Mouysset, Bernard Fraysse","doi":"10.1159/000543916","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>The aims of this study were to measure the effectiveness of hearing aid (HA) fitting in improving understanding in quiet and in noise and to investigate the factors that significantly influence these results. This study will be carried out through a retrospective analysis of the results obtained from patients fitted with HAs at Amplifon HA centers between 2018 and 2021. This study explores and classifies the predictive factors of HAs outcomes, looking at the impact of HA technology, personalized adjustments made by the hearing care professional, and patient follow-up and daily use (data logging).</p><p><strong>Methods: </strong>The study is based on the analysis of a large population of HA users who were fitted in HA centers between 2018 and 2021. It included 77,661 patients. HA outcome is measured through the improvement of intelligibility in quiet and noise. eXtreme Gradient Boosting machine learning method is used to identify predictive factors of HA outcome. SHapley Additive exPlanations Value analysis derived from the game theory is used to evaluate the individual impact of each factor.</p><p><strong>Results: </strong>HA outcomes are significant in terms of both average improvement per patient of speech intelligibility and the percentage of patients improved. The analysis shows that the level of aided speech perception in quiet and noise is impacted by the choice of technology (category level and manufacturer), fitting parameters (amplification level and binaural loudness balancing) as well as by a high therapy adherence. In particular, binaural loudness balancing was shown to be systematically beneficial to all patients.</p><p><strong>Conclusion: </strong>Big data analysis is a new relevant method to evaluate predictive factors for HA outcomes. It demonstrates HA efficiency to improve intelligibility in quiet and noise and shows the impact of hearing care professionals in maximizing patient's outcome through the selection of the most appropriate technology, fitting parameters, and a regular follow-up ensuring a high daily usage. However, global results must be interpreted with caution on such a heterogeneous population. They would need to be refined by an approach using clusters of patients with similar audiological profiles.</p>","PeriodicalId":55432,"journal":{"name":"Audiology and Neuro-Otology","volume":" ","pages":"1-9"},"PeriodicalIF":1.6000,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Audiology and Neuro-Otology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1159/000543916","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AUDIOLOGY & SPEECH-LANGUAGE PATHOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Introduction: The aims of this study were to measure the effectiveness of hearing aid (HA) fitting in improving understanding in quiet and in noise and to investigate the factors that significantly influence these results. This study will be carried out through a retrospective analysis of the results obtained from patients fitted with HAs at Amplifon HA centers between 2018 and 2021. This study explores and classifies the predictive factors of HAs outcomes, looking at the impact of HA technology, personalized adjustments made by the hearing care professional, and patient follow-up and daily use (data logging).

Methods: The study is based on the analysis of a large population of HA users who were fitted in HA centers between 2018 and 2021. It included 77,661 patients. HA outcome is measured through the improvement of intelligibility in quiet and noise. eXtreme Gradient Boosting machine learning method is used to identify predictive factors of HA outcome. SHapley Additive exPlanations Value analysis derived from the game theory is used to evaluate the individual impact of each factor.

Results: HA outcomes are significant in terms of both average improvement per patient of speech intelligibility and the percentage of patients improved. The analysis shows that the level of aided speech perception in quiet and noise is impacted by the choice of technology (category level and manufacturer), fitting parameters (amplification level and binaural loudness balancing) as well as by a high therapy adherence. In particular, binaural loudness balancing was shown to be systematically beneficial to all patients.

Conclusion: Big data analysis is a new relevant method to evaluate predictive factors for HA outcomes. It demonstrates HA efficiency to improve intelligibility in quiet and noise and shows the impact of hearing care professionals in maximizing patient's outcome through the selection of the most appropriate technology, fitting parameters, and a regular follow-up ensuring a high daily usage. However, global results must be interpreted with caution on such a heterogeneous population. They would need to be refined by an approach using clusters of patients with similar audiological profiles.

使用机器学习预测助听器的效果。
本研究的目的是测量助听器在安静和噪音环境下提高理解能力的有效性,并探讨显著影响这些结果的因素。该研究将通过对2018年至2021年在助听器中心安装助听器的患者的结果进行回顾性分析来进行。本研究对患者预后的预测因素进行了探讨和分类,考察了助听器技术的选择(类别水平)、助听器专业人员的个性化调整(放大水平、双耳响度平衡)以及患者的随访和日常使用(数据记录)的影响。方法2018 - 2021年对听力受损人群进行助听器安置。其中包括77,661例患者。在研究的第一部分,对数据进行统计分析,研究各种相关性。然后,使用机器学习和特征解释算法进行预测,特别是基于PyTorch和极限梯度增强(XGBoost)的神经网络。对于解释力,使用SHapley加性解释(SHAP)方法来评估每个变量的个体贡献。结果使用SHAP值和XGBoost预测模型,技术水平对噪声中语音感知(SPIN)和安静中语音感知(SPIQ)得分的影响显著,双耳响度补偿对测试结果的改善效果显著。最后,发现初始语音噪声比(SNR)与调整后的信噪比以及语音识别阈值(SRT)之间存在线性关系。最后,对每天佩戴助听器超过9小时的效果进行了分析,结果显示恢复效果更好。这是一项回顾性研究。大量的数据弥补了这种偏差。结论大数据分析是评估助听器预后预测因素的新方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Audiology and Neuro-Otology
Audiology and Neuro-Otology 医学-耳鼻喉科学
CiteScore
3.20
自引率
6.20%
发文量
35
审稿时长
>12 weeks
期刊介绍: ''Audiology and Neurotology'' provides a forum for the publication of the most-advanced and rigorous scientific research related to the basic science and clinical aspects of the auditory and vestibular system and diseases of the ear. This journal seeks submission of cutting edge research opening up new and innovative fields of study that may improve our understanding and treatment of patients with disorders of the auditory and vestibular systems, their central connections and their perception in the central nervous system. In addition to original papers the journal also offers invited review articles on current topics written by leading experts in the field. The journal is of primary importance for all scientists and practitioners interested in audiology, otology and neurotology, auditory neurosciences and related disciplines.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信