Accelerating Psychometric Screening Tests With Bayesian Active Differential Selection

TL;DR

This paper introduces a Bayesian active model selection method to rapidly detect changes in a patient's psychometric function, significantly reducing measurement time in audiogram testing.

Contribution

It presents a novel Bayesian active differential selection approach for quick and accurate psychometric change detection in audiometric screening.

Findings

High confidence detection of audiometric changes with few tones

Validated approach using NIOSH audiometric data

Reduces measurement time compared to classical methods

Abstract

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.