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

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.

Assessment of text coherence using an ontology‐based relatedness measurement method

“…and Fiorelli, Pazienza, and Stellato (2015)) in annotation tool development, Jadidinejad, Mahmoudi, and Meybodi (2016)) in document classification, and Ali et al (2019b) in knowledge engineering; try to maximize the number of words annotated but do not report any number for annotation rate. We report on our findings regarding the annotation rate in Section 5.4.…”

“…To the best of our knowledge, there is no “gold standard” for the annotation rate. Various studies conducting semantic annotation in various domains, such as Bada, Vasilevsky, Haendel, and Hunter ()) and Beasley and Manda ()) in bioinformatics, Lévy, Tomeh, and Ma ()) and Fiorelli, Pazienza, and Stellato ()) in annotation tool development, Jadidinejad, Mahmoudi, and Meybodi ()) in document classification, and Ali et al () in knowledge engineering; try to maximize the number of words annotated but do not report any number for annotation rate. We report on our findings regarding the annotation rate in Section 5.4.…”