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A knowledge construction methodology to automate case-based learning using clinical documents

Citation

Ali, M and Hussain, J and Lee, S and Kang, BH and Sattar, K, A knowledge construction methodology to automate case-based learning using clinical documents, Expert Systems Article e12401. ISSN 0266-4720 (2019) [Refereed Article]

Copyright Statement

Copyright 2019 John Wiley & Sons, Ltd.

DOI: doi:10.1111/exsy.12401

Abstract

The case-based learning (CBL) approach has gained attention in medical education as an alternative to traditional learning methodology. However, current CBL systems do not facilitate and provide computer-based domain knowledge to medical students for solving real-world clinical cases during CBL practice. To automate CBL, clinical documents are beneficial for constructing domain knowledge. In the literature, most systems and methodologies require a knowledge engineer to construct machine-readable knowledge. Keeping in view these facts, we present a knowledge construction methodology (KCM-CD) to construct domain knowledge ontology (i.e., structured declarative knowledge) from unstructured text in a systematic way using artificial intelligence techniques, with minimum intervention from a knowledge engineer. To utilize the strength of humans and computers, and to realize the KCM-CD methodology, an interactive case-based learning system (iCBLS) was developed. Finally, the developed ontological model was evaluated to evaluate the quality of domain knowledge in terms of coherence measure. The results showed that the overall domain model has positive coherence values, indicating that all words in each branch of the domain ontology are correlated with each other and the quality of the developed model is acceptable.

Item Details

Item Type:Refereed Article
Keywords:case-based learning, clinical case, controlled natural language, declarative knowledge, knowledge engineering, ontological model
Research Division:Information and Computing Sciences
Research Group:Artificial Intelligence and Image Processing
Research Field:Expert Systems
Objective Division:Information and Communication Services
Objective Group:Computer Software and Services
Objective Field:Application Tools and System Utilities
UTAS Author:Ali, M ( Maqbool Ali)
UTAS Author:Kang, BH (Professor Byeong Kang)
ID Code:132278
Year Published:2019
Web of Science® Times Cited:1
Deposited By:Information and Communication Technology
Deposited On:2019-05-01
Last Modified:2019-06-20
Downloads:0

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