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RESEARCH LIBRARY

View the latest publications from members of the NBME research team

Showing 1 - 4 of 4 Research Library Publications
Posted: September 14, 2022 | 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: May 5, 2022 | 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: December 4, 2020 | 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: April 30, 2020 | 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.