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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 Type:	Article
Authors/Creators:	Ali, M and Hussain, J and Lee, S and Kang, BH and Sattar, K
Keywords:	case-based learning, clinical case, controlled natural language, declarative knowledge, knowledge engineering, ontological model
Journal or Publication Title:	Expert Systems
Publisher:	Blackwell Publ Ltd
ISSN:	0266-4720
DOI / ID Number:	https://doi.org/10.1111/exsy.12401
Copyright Information:	Copyright 2019 John Wiley & Sons, Ltd.
Related URLs:	Google Scholar: Kang, BH UTAS Profile: Kang, BH
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