{"title":"通过听觉档案的联合合并整合听力学数据集。","authors":"Samira Saak, Dirk Oetting, Birger Kollmeier, Mareike Buhl","doi":"10.1177/23312165251349617","DOIUrl":null,"url":null,"abstract":"<p><p>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 (<i>N<sub>A</sub></i> = 595) were merged with 31 newly generated APs from a second dataset (<i>N<sub>B</sub></i> = 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.</p>","PeriodicalId":48678,"journal":{"name":"Trends in Hearing","volume":"29 ","pages":"23312165251349617"},"PeriodicalIF":3.0000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12209579/pdf/","citationCount":"0","resultStr":"{\"title\":\"Integrating Audiological Datasets via Federated Merging of Auditory Profiles.\",\"authors\":\"Samira Saak, Dirk Oetting, Birger Kollmeier, Mareike Buhl\",\"doi\":\"10.1177/23312165251349617\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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 (<i>N<sub>A</sub></i> = 595) were merged with 31 newly generated APs from a second dataset (<i>N<sub>B</sub></i> = 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.</p>\",\"PeriodicalId\":48678,\"journal\":{\"name\":\"Trends in Hearing\",\"volume\":\"29 \",\"pages\":\"23312165251349617\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2025-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12209579/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Trends in Hearing\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1177/23312165251349617\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/6/30 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"AUDIOLOGY & SPEECH-LANGUAGE PATHOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Trends in Hearing","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1177/23312165251349617","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/6/30 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"AUDIOLOGY & SPEECH-LANGUAGE PATHOLOGY","Score":null,"Total":0}
Integrating Audiological Datasets via Federated Merging of Auditory Profiles.
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.
Trends in HearingAUDIOLOGY & 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.