Accelerating Psychometric Screening Tests with Prior Information
| Autor: | Dennis L. Barbour, Trevor J. Larsen, Gustavo Malkomes |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Screening test
business.industry Computer science Model selection media_common.quotation_subject Bayesian probability Machine learning computer.software_genre Test (assessment) Psychometric function Audiometry test Artificial intelligence business Function (engineering) computer Prior information media_common |
| Zdroj: | Explainable AI in Healthcare and Medicine ISBN: 9783030533519 |
| DOI: | 10.1007/978-3-030-53352-6_29 |
| Popis: | 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 solutions for rapid high-resolution approximation of the psychometric function of a patient given her or his prior exam. We develop a rapid screening algorithm for a change in the psychometric function estimation of a patient. We use Bayesian active model selection to perform an automated pure-tone audiometry test with the goal of quickly finding if the current estimation will be different from the previous one. We validate our methods using audiometric data from the National Institute for Occupational Safety and Health (niosh). Initial results indicate that with a few tones we can (i) detect if the patient’s audiometric function has changed between the two test sessions with high confidence, and (ii) learn high-resolution approximations of the target psychometric function. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|