Title :
Mining case summaries in BioWorld
Author :
Poitras, Eric ; Doleck, Tenzin ; Lajoie, Susanne
Author_Institution :
McGill Univ., Montreal, QC, Canada
Abstract :
BioWorld is a computer-based learning environment that was designed to support novices in diagnosing medical diseases. In this study, we examine case summaries written in BioWorld. We explore the use of text classification techniques to mine case summaries written in BioWorld. In particular, we evaluate the accuracy of several text classification algorithms in Diagnosis Correctness and Novice-Expert Overlay Model (i.e., recognizing case summaries written by novice and expert physicians). Experimental results suggest that text classification is a promising approach for mining case summaries.
Keywords :
computer aided instruction; data mining; medical diagnostic computing; pattern classification; text analysis; BioWorld; case summary mining; computer-based learning environment; diagnosis correctness model; medical disease diagnosis; novice-expert overlay model; text classification techniques; Computers; Data models; Niobium; Pragmatics; Presses; Transforms; Artificial Intelligence; Case Summaries; Medical Education; Text Classification; Text Mining;
Conference_Titel :
Computer Science & Education (ICCSE), 2014 9th International Conference on
Conference_Location :
Vancouver, BC
Print_ISBN :
978-1-4799-2949-8
DOI :
10.1109/ICCSE.2014.6926421
