RESEARCH LIBRARY
RESEARCH LIBRARY
View the latest publications from members of the NBME research team
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.
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.
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.
Academic Medicine: Volume 95 - Issue 11S - Pages S89-S94
Semiannually, U.S. pediatrics residency programs report resident milestone levels to the Accreditation Council for Graduate Medical Education (ACGME). The Pediatrics Milestones Assessment Collaborative (PMAC) developed workplace-based assessments of 2 inferences. The authors compared learner and program variance in PMAC scores with ACGME milestones.
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.
Medical Teacher: Volume 40 - Issue 11 - p 1143-1150
This study explores a novel milestone-based workplace assessment system that was implemented in 15 pediatrics residency programs. The system provided: web-based multisource feedback and structured clinical observation instruments that could be completed on any computer or mobile device; and monthly feedback reports that included competency-level scores and recommendations for improvement.