Mining case summaries in BioWorld

Author

Poitras, Eric ; Doleck, Tenzin ; Lajoie, Susanne

Author_Institution

McGill Univ., Montreal, QC, Canada

fYear

2014

fDate

22-24 Aug. 2014

Firstpage

6

Lastpage

9

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Science & Education (ICCSE), 2014 9th International Conference on

Conference_Location

Vancouver, BC

Print_ISBN

978-1-4799-2949-8

Type

conf

DOI

10.1109/ICCSE.2014.6926421

Filename

6926421