Building a knowledge base of severe adverse drug events based on AERS reporting data using semantic Web technologies

Guoqian D Jiang, Liwei Wang, Hongfang D Liu, Harold R. Solbrig, Christopher G. Chute

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)

Abstract

A semantically coded knowledge base of adverse drug events (ADEs) with severity information is critical for clinical decision support systems and translational research applications. However it remains challenging to measure and identify the severity information of ADEs. The objective of the study is to develop and evaluate a semantic web based approach for building a knowledge base of severe ADEs based on the FDA Adverse Event Reporting System (AERS) reporting data. We utilized a normalized AERS reporting dataset and extracted putative drug-ADE pairs and their associated outcome codes in the domain of cardiac disorders. We validated the drug-ADE associations using ADE datasets from SIDe Effect Resource (SIDER) and the UMLS. We leveraged the Common Terminology Criteria for Adverse Event (CTCAE) grading system and classified the ADEs into the CTCAE in the Web Ontology Language (OWL). We identified and validated 2,444 unique Drug-ADE pairs in the domain of cardiac disorders, of which 760 pairs are in Grade 5, 775 pairs in Grade 4 and 2,196 pairs in Grade 3.

Original languageEnglish (US)
Title of host publicationStudies in Health Technology and Informatics
Pages496-500
Number of pages5
Volume192
Edition1-2
DOIs
StatePublished - 2013
Event14th World Congress on Medical and Health Informatics, MEDINFO 2013 - Copenhagen, Denmark
Duration: Aug 20 2013Aug 23 2013

Other

Other14th World Congress on Medical and Health Informatics, MEDINFO 2013
CountryDenmark
CityCopenhagen
Period8/20/138/23/13

Fingerprint

Knowledge Bases
Terminology
Semantic Web
Drug-Related Side Effects and Adverse Reactions
Semantics
Information Systems
Research Design
Technology
Decision support systems
Ontology
Unified Medical Language System
Clinical Decision Support Systems
Pharmaceutical Preparations
Translational Medical Research
Language

Keywords

  • Adverse Drug Events
  • Biomedical Ontologies
  • Pharmacogenomics
  • Semantic Web
  • Severity

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

Cite this

Jiang, G. D., Wang, L., Liu, H. D., Solbrig, H. R., & Chute, C. G. (2013). Building a knowledge base of severe adverse drug events based on AERS reporting data using semantic Web technologies. In Studies in Health Technology and Informatics (1-2 ed., Vol. 192, pp. 496-500) https://doi.org/10.3233/978-1-61499-289-9-496

Building a knowledge base of severe adverse drug events based on AERS reporting data using semantic Web technologies. / Jiang, Guoqian D; Wang, Liwei; Liu, Hongfang D; Solbrig, Harold R.; Chute, Christopher G.

Studies in Health Technology and Informatics. Vol. 192 1-2. ed. 2013. p. 496-500.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Jiang, GD, Wang, L, Liu, HD, Solbrig, HR & Chute, CG 2013, Building a knowledge base of severe adverse drug events based on AERS reporting data using semantic Web technologies. in Studies in Health Technology and Informatics. 1-2 edn, vol. 192, pp. 496-500, 14th World Congress on Medical and Health Informatics, MEDINFO 2013, Copenhagen, Denmark, 8/20/13. https://doi.org/10.3233/978-1-61499-289-9-496
Jiang GD, Wang L, Liu HD, Solbrig HR, Chute CG. Building a knowledge base of severe adverse drug events based on AERS reporting data using semantic Web technologies. In Studies in Health Technology and Informatics. 1-2 ed. Vol. 192. 2013. p. 496-500 https://doi.org/10.3233/978-1-61499-289-9-496
Jiang, Guoqian D ; Wang, Liwei ; Liu, Hongfang D ; Solbrig, Harold R. ; Chute, Christopher G. / Building a knowledge base of severe adverse drug events based on AERS reporting data using semantic Web technologies. Studies in Health Technology and Informatics. Vol. 192 1-2. ed. 2013. pp. 496-500
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