Predicting Common Audiological Functional Parameters (CAFPAs) as Interpretable Intermediate Representation in a Clinical Decision-Support System for Audiology
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...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-12-01
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| Series: | Frontiers in Digital Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fdgth.2020.596433/full |
| _version_ | 1818434720538558464 |
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| author | Samira K. Saak Samira K. Saak Andrea Hildebrandt Andrea Hildebrandt Birger Kollmeier Birger Kollmeier Birger Kollmeier Birger Kollmeier Mareike Buhl Mareike Buhl |
| author_facet | Samira K. Saak Samira K. Saak Andrea Hildebrandt Andrea Hildebrandt Birger Kollmeier Birger Kollmeier Birger Kollmeier Birger Kollmeier Mareike Buhl Mareike Buhl |
| author_sort | Samira K. Saak |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-14T16:41:29Z |
| format | Article |
| id | doaj.art-87b488b1e2bb4c16b93cfc9b430afbac |
| institution | Directory Open Access Journal |
| issn | 2673-253X |
| language | English |
| last_indexed | 2024-12-14T16:41:29Z |
| publishDate | 2020-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Digital Health |
| spelling | doaj.art-87b488b1e2bb4c16b93cfc9b430afbac2022-12-21T22:54:19ZengFrontiers Media S.A.Frontiers in Digital Health2673-253X2020-12-01210.3389/fdgth.2020.596433596433Predicting Common Audiological Functional Parameters (CAFPAs) as Interpretable Intermediate Representation in a Clinical Decision-Support System for AudiologySamira K. Saak0Samira K. Saak1Andrea Hildebrandt2Andrea Hildebrandt3Birger Kollmeier4Birger Kollmeier5Birger Kollmeier6Birger Kollmeier7Mareike Buhl8Mareike Buhl9Department of Psychology, Carl von Ossietzky Universität Oldenburg, Oldenburg, GermanyCluster of Excellence Hearing4all, Carl von Ossietzky Universität Oldenburg, Oldenburg, GermanyDepartment of Psychology, Carl von Ossietzky Universität Oldenburg, Oldenburg, GermanyCluster of Excellence Hearing4all, Carl von Ossietzky Universität Oldenburg, Oldenburg, GermanyCluster of Excellence Hearing4all, Carl von Ossietzky Universität Oldenburg, Oldenburg, GermanyMedizinische Physik, Medizinische Physik, Carl von Ossietzky Universität Oldenburg, Oldenburg, GermanyHörTech gGmbH, Oldenburg, GermanyHearing, Speech and Audio Technology, Fraunhofer Institute for Digital Media Technology (IDMT), Oldenburg, GermanyCluster of Excellence Hearing4all, Carl von Ossietzky Universität Oldenburg, Oldenburg, GermanyMedizinische Physik, Medizinische Physik, Carl von Ossietzky Universität Oldenburg, Oldenburg, GermanyThe 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.https://www.frontiersin.org/articles/10.3389/fdgth.2020.596433/fullCAFPAsclinical decision-support systemsmachine learningaudiologyinterpretable machine learningprecision diagnostics |
| spellingShingle | Samira K. Saak Samira K. Saak Andrea Hildebrandt Andrea Hildebrandt Birger Kollmeier Birger Kollmeier Birger Kollmeier Birger Kollmeier Mareike Buhl Mareike Buhl Predicting Common Audiological Functional Parameters (CAFPAs) as Interpretable Intermediate Representation in a Clinical Decision-Support System for Audiology Frontiers in Digital Health CAFPAs clinical decision-support systems machine learning audiology interpretable machine learning precision diagnostics |
| title | Predicting Common Audiological Functional Parameters (CAFPAs) as Interpretable Intermediate Representation in a Clinical Decision-Support System for Audiology |
| title_full | Predicting Common Audiological Functional Parameters (CAFPAs) as Interpretable Intermediate Representation in a Clinical Decision-Support System for Audiology |
| title_fullStr | Predicting Common Audiological Functional Parameters (CAFPAs) as Interpretable Intermediate Representation in a Clinical Decision-Support System for Audiology |
| title_full_unstemmed | Predicting Common Audiological Functional Parameters (CAFPAs) as Interpretable Intermediate Representation in a Clinical Decision-Support System for Audiology |
| title_short | Predicting Common Audiological Functional Parameters (CAFPAs) as Interpretable Intermediate Representation in a Clinical Decision-Support System for Audiology |
| title_sort | predicting common audiological functional parameters cafpas as interpretable intermediate representation in a clinical decision support system for audiology |
| topic | CAFPAs clinical decision-support systems machine learning audiology interpretable machine learning precision diagnostics |
| url | https://www.frontiersin.org/articles/10.3389/fdgth.2020.596433/full |
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