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2018 | OriginalPaper | Buchkapitel

An Adverse Drug Events Ontology Population from Text Using a Multi-class SVM Based Approach

verfasst von : Ons Jabnoun, Hadhemi Achour, Kaouther Nouira

Erschienen in: Digital Economy. Emerging Technologies and Business Innovation

Verlag: Springer International Publishing

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Abstract

In recent years, semantic web technologies and ontologies in particular, are being increasingly used in various e-Health systems and applications. However, issues related to automatically constructing, populating and enriching such ontologies are still outstanding. In this paper, we propose an automatic Adverse Drug Events (ADE) ontology population approach so called ADETermino. The proposed approach is based on Information Extraction methods and mainly aims to extract new concept instances and relationships from textual drug leaflets. It combines a Named-Entity Recognition (NER) system using lexical resources and a machine learning method using a multi-class Support Vector Machine (SVM) classifier for relations detection. Experiments were performed using 102 cardiac drug leaflets corresponding to 5706 input vectors. The results show the performance of our approach with an F-score of 89%.

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Vorheriges Kapitel DAPER Joint Learning from Partially Structured Graph Databases
Nächstes Kapitel E-Learning Effectiveness: A Survey in Two Tunisian Higher Education Establishments Using an Educational Platform
Fußnoten
1
i2b2 (Informatics for Integrating Biology and the Bedside) is “an NIH-funded National Center for Biomedical Computing (NCBC) based at Partners Healthcare System in Boston” [7].
 
Literatur
1.
Handler, S.M., et al.: A systematic review of the performance characteristics of clinical event monitor signals used to detect adverse drug events in the hospital setting. J. Am. Med. Inform. Assoc. 14(4), 451–458 (2007)CrossRef
2.
Nakhla, Z., Nouira, K.: Development of ontology for the representation of adverse drug events of diabetes disease. Int. J. Comput. Appl. 42, 10–16 (2012)
3.
Nouira, K., Nakhla, Z.: Ontology-based cardiac adverse drug event problem prevention. In: 2013 International Conference on Computer Applications Technology (ICCAT), pp. 1–6, January 2013
4.
Drumond, L., Girardi, R.: A survey of ontology learning procedures. In: WONTO, vol. 427, pp. 1–13 (2008)
5.
Nakhla, Z., Nouira, K.: Automatically building database from biomedical ontology. In: International Work-Conference on Bioinformatics and Biomedical Engineering, pp. 1403–1411 (2014)
6.
Torii, M., Wagholikar, K., Liu, H.: Using machine learning for concept extraction on clinical documents from multiple data sources. J. Am. Med. Inform. Assoc. 18(5), 580–587 (2011)CrossRef
7.
Doan, S., Xu, H.: Recognizing medication related entities in hospital discharge summaries using support vector machine. In: Proceedings of the 23rd International Conference on Computational Linguistics: Posters, pp. 259–266. Association for Computational Linguistics, August 2010
8.
Minard, A.L., Ligozat, A.L., Grau, B.: Multi-class SVM for relation extraction from clinical reports. In: RANLP, pp. 604–609, September 2011
9.
Rink, B., Harabagiu, S., Roberts, K.: Automatic extraction of relations between medical concepts in clinical texts. J. Am. Med. Inform. Assoc. 18(5), 594–600 (2011)CrossRef
10.
Roller, R., Stevenson, M.: Applying UMLS for distantly supervised relation detection. In: Proceedings of the 5th International Workshop on Health Text Mining and Information Analysis (Louhi)@ EACL, pp. 80–84, April 2014
11.
Sahu, S.K., Anand, A., Oruganty, K., Gattu, M.: Relation extraction from clinical texts using domain invariant convolutional neural network. In: ACL BioNLP 2016 Workshop (2016)
12.
Aramaki, E., et al.: Extraction of adverse drug effects from clinical records. Stud. Health Technol. Inform 160(Pt 1), 739–743 (2010)MathSciNet
13.
Gurulingappa, H., Mateen-Rajput, A., Toldo, L.: Extraction of potential adverse drug events from medical case reports. J. Biomed. Semant. 3(1), 15 (2012)CrossRef
14.
Kang, N., Singh, B., Bui, C., Afzal, Z., van Mulligen, E.M., Kors, J.A.: Knowledge-based extraction of adverse drug events from biomedical text. BMC Bioinform. 15(1), 1 (2014)CrossRef
15.
Wang, W., Haerian, K., Salmasian, H., Harpaz, R., Chase, H., Friedman, C.: A drug-adverse event extraction algorithm to support pharmacovigilance knowledge mining from PubMed citations. In: AMIA Annual Symposium Proceedings, vol. 2011, pp. 1464–1470, October 2011
16.
Vapnik, V.N., Vapnik, V.: Statistical Learning Theory, vol. 1. Wiley, New York (1998)MATH
17.
Metadaten
Titel
An Adverse Drug Events Ontology Population from Text Using a Multi-class SVM Based Approach
verfasst von
Ons Jabnoun
Hadhemi Achour
Kaouther Nouira
Copyright-Jahr
2018
Buch
Digital Economy. Emerging Technologies and Business Innovation

Print ISBN: 978-3-319-97748-5

Electronic ISBN: 978-3-319-97749-2

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-97749-2_11