Sensitivity and specificity of automatic audiological classification using expert-labelled audiological data and Common Audiological Functional Parameters.
As a step towards the development of an audiological diagnostic supporting tool employing machine learning methods, this article aims at evaluating the classification performance of different audiological measures as well as Common Audiological Functional Parameters (CAFPAs). CAFPAs are designed to...
| Publicado en: | International Journal of Audiology Vol. 60; no. 1; pp. 16 - 27 |
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| Autores principales: | , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
Jan2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=147926931&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 147926931 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14992027 JW2 jtl: International Journal of Audiology issn: 14992027 maglogo: Y pubinfo: dt: Jan2021 vid: 60 iid: 1 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 147926931 146070250 147926931 147926931 10.1080/14992027.2020.1817581 147926931 ppf: 16 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Sensitivity and specificity of automatic audiological classification using expert-labelled audiological data and Common Audiological Functional Parameters. aug: au: Buhl, Mareike Warzybok, Anna Schädler, Marc René Kollmeier, Birger affil: Medizinische Physik, Universität Oldenburg, Oldenburg, Germany sug: subj: Audiology Evaluation Functional Assessment Machine Learning Hearing Disorders Diagnosis Human ROC Curve Sensitivity and Specificity Data Analysis, Statistical Descriptive Statistics Funding Source ab: As a step towards the development of an audiological diagnostic supporting tool employing machine learning methods, this article aims at evaluating the classification performance of different audiological measures as well as Common Audiological Functional Parameters (CAFPAs). CAFPAs are designed to integrate different clinical databases and provide abstract representations of measures. Classification and evaluation of classification performance in terms of sensitivity and specificity are performed on a data set from a previous study, where statistical models of diagnostic cases were estimated from expert-labelled data. The data set contains 287 cases. The classification performance in clinically relevant comparison sets of two competing categories was analysed for audiological measures and CAFPAs. It was found that for different audiological diagnostic questions a combination of measures using different weights of the parameters is useful. A set of four to six measures was already sufficient to achieve maximum classification performance which indicates that the measures contain redundant information. The current set of CAFPAs was confirmed to yield in most cases approximately the same classification performance as the respective optimum set of audiological measures. Overall, the concept of CAFPAs as compact, abstract representation of auditory deficiencies is confirmed. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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