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 well...

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Главные авторы:	Samira K. Saak, Andrea Hildebrandt, Birger Kollmeier, Mareike Buhl
Формат:	Artigo
Язык:	Inglês
Опубликовано:	Frontiers Media S.A. 2020-12-01
Серии:	Frontiers in Digital Health
Предметы:	CAFPAs clinical decision-support systems machine learning audiology interpretable machine learning precision diagnostics
Online-ссылка:	https://www.frontiersin.org/articles/10.3389/fdgth.2020.596433/full
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