Accelerating Psychometric Screening Tests With Bayesian Active Differential Selection

Accelerating Psychometric Screening Tests With Bayesian Active Differential Selection. Larsen, T. J., Malkomes, G., & Barbour, D. L. arXiv:2002.01547 [cs, stat], February, 2020. arXiv: 2002.01547

Paper abstract bibtex

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.

@article{larsen_accelerating_2020,
	title = {Accelerating {Psychometric} {Screening} {Tests} {With} {Bayesian} {Active} {Differential} {Selection}},
	url = {http://arxiv.org/abs/2002.01547},
	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.},
	urldate = {2020-11-11},
	journal = {arXiv:2002.01547 [cs, stat]},
	author = {Larsen, Trevor J. and Malkomes, Gustavo and {Barbour, D. L.}},
	month = feb,
	year = {2020},
	note = {arXiv: 2002.01547},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
}

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