Sensitivity and specificity of automatic audiological classification using expert-labelled audiological data and Common Audiological Functional Parameters

Buhl, Mareike; Warzybok, Anna; Schädler, Marc René; Kollmeier, Birger

doi:10.1080/14992027.2020.1817581

Cited by 4 publications

(15 citation statements)

References 21 publications

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“…On the way of setting up a CDSS using CAFPAs as interpretable, intermediate layer, the current study closes the gap towards a CDSS working with the input data from a single patient: The prediction models trained here can be used in the future to automatically generate CAFPAs, based on which a classification of audiological findings can be performed. The classification performance could be compared to the classification performance based on the labeled CAFPAs from the expert data set ( 57 ).…”

Section: Discussionmentioning

confidence: 99%

Predicting Common Audiological Functional Parameters (CAFPAs) as Interpretable Intermediate Representation in a Clinical Decision-Support System for Audiology

Saak

Hildebrandt

Kollmeier

et al. 2020

Front. Digit. Health

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The application of machine learning for the development of clinical decision-support systems in audiology provides the potential to improve the objectivity and precision of clinical experts' diagnostic decisions. However, for successful clinical application, such a tool needs to be accurate, as well as accepted and trusted by physicians. In the field of audiology, large amounts of patients' data are being measured, but these are distributed over local clinical databases and are heterogeneous with respect to the applied assessment tools. For the purpose of integrating across different databases, the Common Audiological Functional Parameters (CAFPAs) were recently established as abstract representations of the contained audiological information describing relevant functional aspects of the human auditory system. As an intermediate layer in a clinical decision-support system for audiology, the CAFPAs aim at maintaining interpretability to the potential users. Thus far, the CAFPAs were derived by experts from audiological measures. For designing a clinical decision-support system, in a next step the CAFPAs need to be automatically derived from available data of individual patients. Therefore, the present study aims at predicting the expert generated CAFPA labels using three different machine learning models, namely the lasso regression, elastic nets, and random forests. Furthermore, the importance of different audiological measures for the prediction of specific CAFPAs is examined and interpreted. The trained models are then used to predict CAFPAs for unlabeled data not seen by experts. Prediction of unlabeled cases is evaluated by means of model-based clustering methods. Results indicate an adequate prediction of the ten distinct CAFPAs. All models perform comparably and turn out to be suitable choices for the prediction of CAFPAs. They also generalize well to unlabeled data. Additionally, the extracted relevant features are plausible for the respective CAFPAs, facilitating interpretability of the predictions. Based on the trained models, a prototype of a clinical decision-support system in audiology can be implemented and extended towards clinical databases in the future.

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Section: Discussionmentioning

confidence: 99%

Predicting Common Audiological Functional Parameters (CAFPAs) as Interpretable Intermediate Representation in a Clinical Decision-Support System for Audiology

Saak

Hildebrandt

Kollmeier

et al. 2020

Front. Digit. Health

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“…Thereby, CAFPAs and diagnostic cases were estimated for given patient cases from a pre-clinical database from Hörzentrum Oldenburg. Based on this data set, Buhl et al [ 32 ] showed that a similar classification performance is obtained using expert-estimated CAFPAs, compared to directly using the audiological measurements (cf. Figure 1 B, left and middle part).…”

Section: Introductionmentioning

confidence: 87%

“…Figure 1 B, left and middle part). However, the classification of [ 32 ] has, thus far, not been applicable to patients other than those contained in the expert-labeled data set, because no quantitative link between measurement outcomes and CAFPAs has been available. Hence, CAFPAs could not be estimated for new patients.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, by combining these previous steps [ 32 , 33 ], the current study aims at exploring whether a CDSS can be constructed for audiology which is as interpretable as expert knowledge-based classification and as data-driven as machine learning-based classification. In the CDSS, CAFPAs are to be estimated from the available audiological measurements, and classification of diagnostic cases (audiological findings or treatment recommendations) needs to be performed based on predicted CAFPAs (cf.…”

Section: Introductionmentioning

confidence: 99%

“…Figure 1 B, right part). In addition, the interpretability of the CDSS needs to be assured, which can partly be combined from the interpretability aspects from the classification of [ 32 ] and CAFPA prediction of [ 33 ].…”

Section: Introductionmentioning

confidence: 99%

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Interpretable Clinical Decision Support System for Audiology Based on Predicted Common Audiological Functional Parameters (CAFPAs)

Buhl

2022

Diagnostics

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Common Audiological Functional Parameters (CAFPAs) were previously introduced as abstract, measurement-independent representation of audiological knowledge, and expert-estimated CAFPAs were shown to be applicable as an interpretable intermediate layer in a clinical decision support system (CDSS). Prediction models for CAFPAs were built based on expert knowledge and one audiological database to allow for data-driven estimation of CAFPAs for new, individual patients for whom no expert-estimated CAFPAs are available. Based on the combination of these components, the current study explores the feasibility of constructing a CDSS which is as interpretable as expert knowledge-based classification and as data-driven as machine learning-based classification. To test this hypothesis, the current study investigated the equivalence in performance of predicted CAFPAs compared to expert-estimated CAFPAs in an audiological classification task, analyzed the importance of different CAFPAs for high and comparable performance, and derived explanations for differences in classified categories. Results show that the combination of predicted CAFPAs and statistical classification enables to build an interpretable but data-driven CDSS. The classification provides good accuracy, with most categories being correctly classified, while some confusions can be explained by the properties of the employed database. This could be improved by including additional databases in the CDSS, which is possible within the presented framework.

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Die Entwicklung der Audiologie - von Helmholtz bis heute

Kießling

2021

Zeitschrift für Medizinische Physik

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Sensitivity and specificity of automatic audiological classification using expert-labelled audiological data and Common Audiological Functional Parameters

Cited by 4 publications

References 21 publications

Predicting Common Audiological Functional Parameters (CAFPAs) as Interpretable Intermediate Representation in a Clinical Decision-Support System for Audiology

Predicting Common Audiological Functional Parameters (CAFPAs) as Interpretable Intermediate Representation in a Clinical Decision-Support System for Audiology

Interpretable Clinical Decision Support System for Audiology Based on Predicted Common Audiological Functional Parameters (CAFPAs)

Die Entwicklung der Audiologie - von Helmholtz bis heute

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