Extraction of Potential Adverse Drug Events from Medical Case Reports

Overview

Journal J Biomed Semantics

Publisher Biomed Central

Specialties Biology
Biomedical Engineering

Date 2012 Dec 22

PMID 23256479

Citations 57

Authors

Harsha Gurulingappa

Abdul Mateen-Rajput

Luca Toldo

Affiliations

Soon will be listed here.

Abstract

: The sheer amount of information about potential adverse drug events published in medical case reports pose major challenges for drug safety experts to perform timely monitoring. Efficient strategies for identification and extraction of information about potential adverse drug events from free-text resources are needed to support pharmacovigilance research and pharmaceutical decision making. Therefore, this work focusses on the adaptation of a machine learning-based system for the identification and extraction of potential adverse drug event relations from MEDLINE case reports. It relies on a high quality corpus that was manually annotated using an ontology-driven methodology. Qualitative evaluation of the system showed robust results. An experiment with large scale relation extraction from MEDLINE delivered under-identified potential adverse drug events not reported in drug monographs. Overall, this approach provides a scalable auto-assistance platform for drug safety professionals to automatically collect potential adverse drug events communicated as free-text data.

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References

Roberts A, Gaizauskas R, Hepple M, Davis N, Demetriou G, Guo Y . The CLEF corpus: semantic annotation of clinical text. AMIA Annu Symp Proc. 2008; :625-9. PMC: 2655900. View

Benton A, Ungar L, Hill S, Hennessy S, Mao J, Chung A . Identifying potential adverse effects using the web: a new approach to medical hypothesis generation. J Biomed Inform. 2011; 44(6):989-96. PMC: 4404640. DOI: 10.1016/j.jbi.2011.07.005. View

Wang X, Hripcsak G, Markatou M, Friedman C . Active computerized pharmacovigilance using natural language processing, statistics, and electronic health records: a feasibility study. J Am Med Inform Assoc. 2009; 16(3):328-37. PMC: 2732239. DOI: 10.1197/jamia.M3028. View

Hauben M, Bate A . Decision support methods for the detection of adverse events in post-marketing data. Drug Discov Today. 2009; 14(7-8):343-57. DOI: 10.1016/j.drudis.2008.12.012. View

Gurulingappa H, Rajput A, Roberts A, Fluck J, Hofmann-Apitius M, Toldo L . Development of a benchmark corpus to support the automatic extraction of drug-related adverse effects from medical case reports. J Biomed Inform. 2012; 45(5):885-92. DOI: 10.1016/j.jbi.2012.04.008. View

Delamarre D, Lillo-Le Louet A, Guillot L, Jamet A, Sadou E, Ouazine T . Documentation in pharmacovigilance: using an ontology to extend and normalize Pubmed queries. Stud Health Technol Inform. 2010; 160(Pt 1):518-22. View

Kuhn M, Campillos M, Letunic I, Jensen L, Bork P . A side effect resource to capture phenotypic effects of drugs. Mol Syst Biol. 2010; 6:343. PMC: 2824526. DOI: 10.1038/msb.2009.98. View

Aramaki E, Miura Y, Tonoike M, Ohkuma T, Masuichi H, Waki K . Extraction of adverse drug effects from clinical records. Stud Health Technol Inform. 2010; 160(Pt 1):739-43. View

Gurulingappa H, Toldo L, Rajput A, Kors J, Taweel A, Tayrouz Y . Automatic detection of adverse events to predict drug label changes using text and data mining techniques. Pharmacoepidemiol Drug Saf. 2013; 22(11):1189-94. DOI: 10.1002/pds.3493. View

10.

Roberts A, Gaizauskas R, Hepple M, Demetriou G, Guo Y, Roberts I . Building a semantically annotated corpus of clinical texts. J Biomed Inform. 2009; 42(5):950-66. DOI: 10.1016/j.jbi.2008.12.013. View

11.

Hanisch D, Fundel K, Mevissen H, Zimmer R, Fluck J . ProMiner: rule-based protein and gene entity recognition. BMC Bioinformatics. 2005; 6 Suppl 1:S14. PMC: 1869006. DOI: 10.1186/1471-2105-6-S1-S14. View

12.

Tikk D, Thomas P, Palaga P, Hakenberg J, Leser U . A comprehensive benchmark of kernel methods to extract protein-protein interactions from literature. PLoS Comput Biol. 2010; 6:e1000837. PMC: 2895635. DOI: 10.1371/journal.pcbi.1000837. View

13.

Vandenbroucke J . In defense of case reports and case series. Ann Intern Med. 2001; 134(4):330-4. DOI: 10.7326/0003-4819-134-4-200102200-00017. View

14.

Knox C, Law V, Jewison T, Liu P, Ly S, Frolkis A . DrugBank 3.0: a comprehensive resource for 'omics' research on drugs. Nucleic Acids Res. 2010; 39(Database issue):D1035-41. PMC: 3013709. DOI: 10.1093/nar/gkq1126. View

15.

Henegar C, Bousquet C, Lillo-Le Louet A, Degoulet P, Jaulent M . Building an ontology of adverse drug reactions for automated signal generation in pharmacovigilance. Comput Biol Med. 2005; 36(7-8):748-67. DOI: 10.1016/j.compbiomed.2005.04.009. View

16.

Merrill G . The MedDRA paradox. AMIA Annu Symp Proc. 2008; :470-4. PMC: 2655972. View

17.

van Mulligen E, Fourrier-Reglat A, Gurwitz D, Molokhia M, Nieto A, Trifiro G . The EU-ADR corpus: annotated drugs, diseases, targets, and their relationships. J Biomed Inform. 2012; 45(5):879-84. DOI: 10.1016/j.jbi.2012.04.004. View