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2018 | OriginalPaper | Chapter

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

Authors : Ons Jabnoun, Hadhemi Achour, Kaouther Nouira

Published in: Digital Economy. Emerging Technologies and Business Innovation

Publisher: 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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Footnotes
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].
 
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Metadata
Title
An Adverse Drug Events Ontology Population from Text Using a Multi-class SVM Based Approach
Authors
Ons Jabnoun
Hadhemi Achour
Kaouther Nouira
Copyright Year
2018
Book
Digital Economy. Emerging Technologies and Business Innovation

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

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

Copyright Year: 2018

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

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