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.
Teaching and Learning in Medicine: Volume 33 - Issue 4 - p 366-381
CSE scores for students from eight schools that moved Step 1 after core clerkships between 2012 and 2016 were analyzed in a pre-post format. Hierarchical linear modeling was used to quantify the effect of the curriculum on CSE performance. Additional analysis determined if clerkship order impacted clinical subject exam performance and whether the curriculum change resulted in more students scoring in the lowest percentiles before and after the curricular change.
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.
American Journal of Obstetrics and Gynecology, Volume 223, Issue 3, Pages 435.e1-435.e6
The purpose of this study was to examine medical student reporting of electronic health record use during the obstetrics and gynecology clerkship.
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.
J Gen Intern Med 34, 705–711 (2019)
This study examines medical student accounts of EHR use during their internal medicine (IM) clerkships and sub-internships during a 5-year time period prior to the new clinical documentation guidelines.