Development of parallel forms of a brief smell identification test useful for longitudinal testing.

ABSTRACT: Although there are numerous brief odor identification tests available for quantifying the ability to smell, none are available in multiple parallel forms that can be longitudinally administered without potential confounding from knowledge of prior test items. Moreover, empirical algorithms for establishing optimal test lengths have not been generally applied. In this study, we employed and compared eight machine learning algorithms to develop a set of four brief parallel smell tests employing items from the University of Pennsylvania Smell Identification Test that optimally differentiated 100 COVID-19 patients from 132 healthy controls. Among the algorithms, linear discriminant analysis (LDA) achieved the best overall performance. The minimum number of odorant test items needed to differen

SUBMITTER: Moein ST 

PROVIDER: S-EPMC10038376 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature