Integrating Audiological Datasets via Federated Merging of Auditory Profiles.

IF 3 2区 医学 Q1 AUDIOLOGY & SPEECH-LANGUAGE PATHOLOGY
Trends in Hearing Pub Date : 2025-01-01 Epub Date: 2025-06-30 DOI:10.1177/23312165251349617
Samira Saak, Dirk Oetting, Birger Kollmeier, Mareike Buhl
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引用次数: 0

Abstract

Audiological datasets contain valuable knowledge about hearing loss in patients, which can be uncovered using data-driven techniques. Our previous approach summarized patient information from one audiological dataset into distinct Auditory Profiles (APs). To obtain a better estimate of the audiological patient population, however, patient patterns must be analyzed across multiple, separated datasets, and finally, be integrated into a combined set of APs. This study aimed at extending the existing profile generation pipeline with an AP merging step, enabling the combination of APs from different datasets based on their similarity across audiological measures. The 13 previously generated APs (NA = 595) were merged with 31 newly generated APs from a second dataset (NB = 1,272) using a similarity score derived from the overlapping densities of common features across the two datasets. To ensure clinical applicability, random forest models were created for various scenarios, encompassing different combinations of audiological measures. A new set with 13 combined APs is proposed, providing separable profiles, which still capture detailed patient information from various test outcome combinations. The classification performance across these profiles is satisfactory. The best performance was achieved using a combination of loudness scaling, audiogram, and speech test information, while single measures performed worst. The enhanced profile generation pipeline demonstrates the feasibility of combining APs across datasets, which should generalize to all datasets and could lead to an interpretable global profile set in the future. The classification models maintain clinical applicability.

通过听觉档案的联合合并整合听力学数据集。
听力学数据集包含有关患者听力损失的宝贵知识,可以使用数据驱动技术发现这些知识。我们之前的方法将来自一个听力学数据集的患者信息汇总为不同的听觉谱(APs)。然而,为了更好地估计听力学患者群体,必须跨多个独立的数据集分析患者模式,最后将其整合到一组ap中。本研究旨在通过AP合并步骤扩展现有的剖面生成管道,使来自不同数据集的AP能够基于其在听力学测量中的相似性进行组合。使用从两个数据集的共同特征重叠密度得出的相似性评分,将先前生成的13个ap (NA = 595)与来自第二个数据集(NB = 1,272)的31个新生成ap合并。为了保证临床的适用性,我们针对不同的场景创建了随机森林模型,包括不同的听力学测量组合。提出了一套新的13个组合ap,提供可分离的配置文件,仍然从各种测试结果组合中捕获详细的患者信息。这些概要文件的分类性能是令人满意的。使用响度缩放、听力图和语音测试信息的组合实现了最佳性能,而单一测量的性能最差。增强的配置文件生成管道证明了跨数据集组合ap的可行性,这应该推广到所有数据集,并可能在未来产生可解释的全局配置文件集。该分类模型保持了临床适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Trends in Hearing
Trends in Hearing AUDIOLOGY & SPEECH-LANGUAGE PATHOLOGYOTORH-OTORHINOLARYNGOLOGY
CiteScore
4.50
自引率
11.10%
发文量
44
审稿时长
12 weeks
期刊介绍: Trends in Hearing is an open access journal completely dedicated to publishing original research and reviews focusing on human hearing, hearing loss, hearing aids, auditory implants, and aural rehabilitation. Under its former name, Trends in Amplification, the journal established itself as a forum for concise explorations of all areas of translational hearing research by leaders in the field. Trends in Hearing has now expanded its focus to include original research articles, with the goal of becoming the premier venue for research related to human hearing and hearing loss.
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