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

ABSTRACT: 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 h

SUBMITTER: Saak SK 

PROVIDER: S-EPMC8521966 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature