Common Audiological Functional Parameters (CAFPAs) for single patient cases: deriving statistical models from an expert-labelled data set.

Objective: Statistical knowledge about many patients could be exploited using machine learning to provide supporting information to otolaryngologists and other hearing health care professionals, but needs to be made accessible. The Common Audiological Functional Parameters (CAFPAs) were recently int...

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Publicado en:International Journal of Audiology Vol. 59; no. 7; pp. 534 - 548
Autores principales: Buhl, Mareike, Warzybok, Anna, Schädler, Marc René, Majdani, Omid, Kollmeier, Birger
Formato: equations & formulas research tables/charts Journal Article
Publicado: Taylor & Francis Ltd Jul2020
Acceso en línea:Ver este registro en EBSCOhost
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        14992027
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      jtl: International Journal of Audiology
      issn: 14992027
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      dt: Jul2020
      vid: 59
      iid: 7
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      pub: Taylor & Francis Ltd
      place: Philadelphia, Pennsylvania
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        10.1080/14992027.2020.1728401
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        atl: Common Audiological Functional Parameters (CAFPAs) for single patient cases: deriving statistical models from an expert-labelled data set.
      aug:
        au:
          Buhl, Mareike
          Warzybok, Anna
          Schädler, Marc René
          Majdani, Omid
          Kollmeier, Birger
        affil: Medizinische Physik, Universität Oldenburg, Oldenburg, Germany
      sug:
        subj:
          Models, Statistical
          Audiology
          Functional Assessment Methods
          Minimum Data Set
          Human
          Funding Source
          Probability
          Descriptive Statistics
          Age Factors
          Hearing Disorders
      ab: Objective: Statistical knowledge about many patients could be exploited using machine learning to provide supporting information to otolaryngologists and other hearing health care professionals, but needs to be made accessible. The Common Audiological Functional Parameters (CAFPAs) were recently introduced for the purpose of integrating data from different databases by providing an abstract representation of audiological measurements. This paper aims at collecting expert labels for a sample database and to determine statistical models from the labelled data set. Design: By an expert survey, CAFPAs as well as labels for audiological findings and treatment recommendations were collected for patients from the database of Hörzentrum Oldenburg. Study sample: A total of 287 single patient cases were assessed by twelve highly experienced audiological experts. Results: The labelled data set was used to derive probability density functions for categories given by the expert labels. The collected data set is suitable for estimating training distributions due to realistic variability contained in data for different, distinct categories. Suitable distribution functions were determined. The derived training distributions were compared regarding different audiological questions. Conclusions: The method-expert survey, sorting data into categories, and determining training distributions – could be extended to other data sets, which could then be integrated via the CAFPAs and used in a classification task.
      pubtype: Academic Journal
      doctype:
        equations & formulas
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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