Showing 1 - 6 of 6 Research Library Publications
Posted: | Erfan Khalaji, Sukru Eraslan, Yeliz Yesilada, Victoria Yaneva

Behavior & Information Technology

 

This study builds upon prior work in this area that focused on developing a machine-learning classifier trained on gaze data from web-related tasks to detect ASD in adults. Using the same data, we show that a new data pre-processing approach, combined with an exploration of the performance of different classification algorithms, leads to an increased classification accuracy compared to prior work.

Posted: | Victoria Yaneva, Brian E. Clauser, Amy Morales, Miguel Paniagua

Advances in Health Sciences Education: Volume 27, p 1401–1422

 

After collecting eye-tracking data from 26 students responding to clinical MCQs, analysis is performed by providing 119 eye-tracking features as input for a machine-learning model aiming to classify correct and incorrect responses. The predictive power of various combinations of features within the model is evaluated to understand how different feature interactions contribute to the predictions.

Posted: | Sukru Eraslan, Yeliz Yesilada, Victoria Yaneva, Simon Harper

ACM SIGACCESS Accessibility and Computing

 

In this article, we first summarise STA (Scanpath Trend Analysis) with its application in autism detection, and then discuss future directions for this research.

Posted: | V. Yaneva, L. A. Ha, S. Eraslan, Y. Yesilada, R. Mitkov

IEEE Transactions on Neural Systems and Rehabilitation Engineering

 

The purpose of this study is to test whether visual processing differences between adults with and without high-functioning autism captured through eye tracking can be used to detect autism.

Posted: | S. H. Felgoise, R. A. Feinberg, H. B. Stephens, P. Barkhaus, K. Boylan, J. Caress, Z. Simmons

Muscle Nerve, 58: 646-654

 

The Amyotrophic Lateral Sclerosis (ALS)‐Specific Quality of Life instrument and its revised version (ALSSQOL and ALSSQOL‐R) have strong psychometric properties, and have demonstrated research and clinical utility. This study aimed to develop a short form (ALSSQOL‐SF) suitable for limited clinic time and patient stamina.

Posted: | M. C. Edwards, A. Slagle, J. D. Rubright, R. J. Wirth

Qual Life Res 27, 1711–1720 (2018)

 

The US Food and Drug Administration (FDA), as part of its regulatory mission, is charged with determining whether a clinical outcome assessment (COA) is “fit for purpose” when used in clinical trials to support drug approval and product labeling. This paper provides a review (and some commentary) on the current state of affairs in COA development/evaluation/use with a focus on one aspect: How do you know you are measuring the right thing? In the psychometric literature, this concept is referred to broadly as validity and has itself evolved over many years of research and application.