Accelerating Psychometric Screening Tests With Bayesian Active Differential Selection

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.

Contrast response function estimation with nonparametric Bayesian active learning

“…The MLCSF inductive bias was not as well matched to canonical phenotypes, leading to slower convergence than quickCSF, but its flexibility allowed it to achieve approximately the same average accuracy on both experiments. Many other options exist to tune the MLCSF inductive bias, including other implementations of CSF priors, mutually conjoint estimation of multiple CSF curves ( Barbour et al, 2018 ; Heisey, Buchbinder, & Barbour, 2018 ), multiplexed detection tasks ( Gardner, Song, Cunningham, Barbour, & Weinberger, 2015 ), and perhaps the most efficient of all, Bayesian active model selection from among predefined phenotypes ( Gardner, Malkomes, et al, 2015 ; Larsen, Malkomes, & Barbour, 2020 ; Larsen, Malkomes, & Barbour, 2021 ).…”