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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Abacha, A.B., Zweigenbaum, P.: Automatic extraction of semantic relations between medical entities: a rule based approach. J. Biomed. Semant. 2(Suppl 5): S4 (2011). http://www.jbiomedsem.com/content/2/S5/S4
Aloise, D., Deshpande, A., Hansen, P., Popat, P.: NP-hardness of Euclidean sum-of-squares clustering. Mach. Learn. 75, 245–249 (2009)
Carlson, L., Marcu, D., Okurowski, M.E.: Building a discourse-tagged corpus in the framework of rhetorical structure theory. In: Current Directions in Discourse and Dialogue, pp. 85−112 (2003)
Chanlekha, H., Kawtrakul, A.: Thai named entity extraction by incorporating maximum entropy model with simple heuristic information. In: Proceedings of IJCNLP’ 2004 (2004)
Chareonsuk, J ., Sukvakree, T., Kawtrakul, A.: Elementary discourse unit segmentation for Thai using discourse cue and syntactic information. In: Proceedings of NCSEC 2005 (2005)
Guthrie, J. A., Guthrie, L., Wilks, Y., Aidinejad, H.: Subject-dependent co-occurrence and word sense disambiguation. In: Proceedings of the 29th Annual Meeting on Association for Computational Linguistics (1991)
Mitchell, T.M.: Machine Learning. The McGraw-Hill Companies Inc. and MIT Press, Singapore (1997)
Rosario, B.: Extraction of semantic relations from bioscience text. A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Information Management and Systems. University of California, Berkeley (2005)
Song, S.-K., Oh, H.-S., Myaeng, S.H., Choi, S.-P., Chun, H.-W., Choi, Y.-S., Jeong, C.-H.: Procedural knowledge extraction on MEDLINE. In: AMT 2011, LNCS 6890, pp. 345–354 (2011)
Sudprasert, S., Kawtrakul, A.: Thai word segmentation based on global and local unsupervised learning. In: Proceedings of NCSEC’2003 (2003)
Acknowledgment
This work has been supported by the Thai Research Fund grant MRG5580030.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-19090-7_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19089-1
Online ISBN: 978-3-319-19090-7
eBook Packages: Computer ScienceComputer Science (R0)