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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Detalles Bibliográficos
Autores principales: Saak, Samira K., Hildebrandt, Andrea, Kollmeier, Birger, Buhl, Mareike
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Digital Health
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8521966/
https://www.ncbi.nlm.nih.gov/pubmed/34713064
http://dx.doi.org/10.3389/fdgth.2020.596433

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8521966/
https://www.ncbi.nlm.nih.gov/pubmed/34713064
http://dx.doi.org/10.3389/fdgth.2020.596433

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