AI Medical Compendium Journal:
International journal of audiology

Showing 1 to 4 of 4 articles

Robust machine learning method for imputing missing values in audiograms collected in children.

International journal of audiology
OBJECTIVE: To assess the accuracy and reliability of a machine learning (ML) algorithm for predicting the full audiograms of hearing-impaired children relative to the common approach (CA).

Common Audiological Functional Parameters (CAFPAs) for single patient cases: deriving statistical models from an expert-labelled data set.

International journal of audiology
Statistical knowledge about many patients could be exploited using machine learning to provide supporting information to otolaryngologists and other hearing health care professionals, but needs to be made accessible. The Common Audiological Function...

Objective auditory brainstem response classification using machine learning.

International journal of audiology
OBJECTIVE: The objective of this study was to use machine learning in the form of a deep neural network to objectively classify paired auditory brainstem response waveforms into either: 'clear response', 'inconclusive' or 'response absent'.

Common Audiological Functional Parameters (CAFPAs): statistical and compact representation of rehabilitative audiological classification based on expert knowledge.

International journal of audiology
OBJECTIVE: As a step towards objectifying audiological rehabilitation and providing comparability between different test batteries and clinics, the Common Audiological Functional Parameters (CAFPAs) were introduced as a common and abstract representa...