RESEARCH LIBRARY

Diagnosis: Volume 10, Issue 1, Pages 54-60

This op-ed discusses the advantages of leveraging natural language processing (NLP) in the assessment of clinical reasoning. It also provides an overview of INCITE, the Intelligent Clinical Text Evaluator, a scalable NLP-based computer-assisted scoring system that was developed to measure clinical reasoning ability as assessed in the written documentation portion of the now-discontinued USMLE Step 2 Clinical Skills examination. 

Academic Medicine: Volume 97 - Issue 11S - Page S176

As Step 1 begins to transition to pass/fail, it is interesting to consider the impact of score goal on wellness. This study examines the relationship between goal score, gender, and students’ self-reported anxiety, stress, and overall distress immediately following their completion of Step 1.

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.

Journal of Graduate Medical Education: Volume 14, Issue 3, Pages 353-354

Letter to the editor.

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.