Accelerating Psychometric Screening Tests With Bayesian Active Differential Selection
This work addresses the need for faster screening tests in audiology, though it appears incremental as it builds on existing Bayesian active learning approaches.
The paper tackled the problem of slow psychometric function estimation by proposing a Bayesian active model selection method to rapidly detect changes in a patient's audiogram, achieving high confidence detection with only a few tones.
Classical methods for psychometric function estimation either require excessive measurements or produce only a low-resolution approximation of the target psychometric function. In this paper, we propose a novel solution for rapid screening for a change in the psychometric function estimation of a given patient. We use Bayesian active model selection to perform an automated pure-tone audiogram test with the goal of quickly finding if the current audiogram will be different from a previous audiogram. We validate our approach using audiometric data from the National Institute for Occupational Safety and Health NIOSH. Initial results show that with a few tones we can detect if the patient's audiometric function has changed between the two test sessions with high confidence.