Abstract
This paper aims to extract the relation between the disease symptoms and the treatments (called the symptom-treatment relation), from hospital-web-board documents to construct the problem-solving map which benefits inexpert people to solve their health problems in preliminary. Both symptoms and treatments expressed on documents are based on several EDUs (elementary discourse units). Our research contains three problems: first, how to identify a symptom-concept-EDU and a treatment-concept EDU. Second, how to determine a symptom-concept-EDU boundary and a treatment-concept-EDU boundary. Third, how to determine the symptom-treatment relation from documents. Therefore, we apply a word co-occurrence to identify a disease-symptom-concept/treatment-concept EDU and Naïve Bayes to determine a disease-symptom-concept boundary and a treatment-concept boundary. We propose using k-mean and Naïve Bayes to determine the symptom-treatment relation from documents with two feature sets, a symptom-concept-EDU group and a treatment-concept-EDU group. Finally, the research achieves 87.5 % precision and 75.4 % recall of the symptom-treatment relation extraction along with the problem-solving map construction.
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This work has been supported by the Thai Research Fund grant MRG5580030.
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Pechsiri, C., Moolwat, O., Piriyakul, R. (2016). Using Extracted Symptom-Treatment Relation from Texts to Construct Problem-Solving Map. In: Skulimowski, A., Kacprzyk, J. (eds) Knowledge, Information and Creativity Support Systems: Recent Trends, Advances and Solutions. Advances in Intelligent Systems and Computing, vol 364. Springer, Cham. https://doi.org/10.1007/978-3-319-19090-7_6
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DOI: https://doi.org/10.1007/978-3-319-19090-7_6
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