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...
Main Authors: | Samira K. Saak, Andrea Hildebrandt, Birger Kollmeier, Mareike Buhl |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2020-12-01
|
Series: | Frontiers in Digital Health |
Subjects: | |
Online Access: | https://www.frontiersin.org/articles/10.3389/fdgth.2020.596433/full |
Similar Items
-
Contribution of Low-Level Acoustic and Higher-Level Lexical-Semantic Cues to Speech Recognition in Noise and Reverberation
by: Anna Warzybok, et al.
Published: (2021-07-01) -
The Hearpiece database of individual transfer functions of an in-the-ear earpiece for hearing device research
by: Denk Florian, et al.
Published: (2021-01-01) -
Preventive audiology An African perspective
Published: (2022) -
Pediatric Audiological Evaluation
by: Elangovan, Saravanan, et al.
Published: (2015) -
Education in Audiology
by: Durrant, J., et al.
Published: (2005)