Extraction of potential adverse drug events from medical case reports

Journal of Biomedical Semantics

Volumn 3, Issue 1, 2012, Pages

  Gurulingappa, Harsha ^a   Mateen Rajpu, Abdul ^b   Toldo, Luca ^b  

^a Molecular Connections Pvt Ltd   (India)

^b MERCK KGAA   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84942412129     PISSN: None     EISSN: 20411480     Source Type: Journal    
DOI: 10.1186/2041-1480-3-15     Document Type: Article

References (25)

1. Hauben M, Bate A: Decision support methods for the detection of adverse events inpost-marketing data. Drug Discov Today. 2009, 14 (7-8): 343-357. 10.1016/j.drudis.2008.12.012.
2. Vandenbroucke JP: In defense of case reports and case series. Ann Intern Med. 2001, 134 (4): 330-334.
3. 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): 328-337. 10.1197/jamia.M3028.
4. Leaman R, Wojtulewicz L, Sullivan R, Skariah A, Yang J, Gonzalez G: Towards internet-age pharmacovigilance: extracting adverse drugreactions from user posts to health-related social networks. Proceedings of the 2010 Workshop on Biomedical Natural LanguageProcessing. Edited by: Dina Demner-Fushman K, Cohen Bretonnel, Ananiadou Sophia, PestianJohn, Tsujii Jun'ichi, Webber Bonnie. 2010, Uppsala, Sweden, 117-125. http://delivery.acm.org/10.1145/1870000/1869976/p117-leaman.pdf
5. Gurulingappa H, Fluck J, Hofmann-Apitius M, Toldo L: Identification of Adverse Drug Event Assertive Sentences in Medical CaseReports. First International Workshop on Knowledge Discovery and Health CareManagement (KD-HCM), European Conference on Machine Learning andPrinciples and Practice of Knowledge Discovery in Databases (ECML PKDD). Edited by: Rangwala H, Tagarelli A, Wale N, Karypis G. 2011, Athens, Greece, 16-27-16-27. http://www.cs.gmu.edu/hrangwal/kd-hcm/proc/KDHCM11_procs.pdf
6. Henegar C, Bousquet C, Lillo-Le Louet A, Degoulet P, Jaulent MC: Building an ontology of adverse drug reactions for automated signalgeneration in pharmacovigilance. Comput Biol Med. 2006, 36: 748-767. 10.1016/j.compbiomed.2005.04.009.
7. Delamarre D, Lillo-Le Louët A, Guillot L, Jamet A, Sadou E, Ouazine T, Burgun A, Jaulent MC: Documentation in pharmacovigilance: using an ontology to extend and normalizePubmed queries. Stud Health Technol Inform. 2010, 160 (Pt 1): 518-522.
8. Aramaki E, Miura Y, Tonoike M, Ohkuma T, Masuichi H, Waki K, Ohe K: Extraction of adverse drug effects from clinical records. MEDINFO 2010 - Proceedings of the 13th World Congress on Medicalinformatics, Series: Studies Health Technology Informatics, Volume 160. Edited by: Safran C. 2010, Cape Town, South Africa: IOS Press, 739-743-739-743. 10.3233/978. -1-60750-588-4-739
9. Benton A, Ungar L, Hill S, Hennessy S, Mao J, Chung A, Leonard C, Holmes J: Identifying potential adverse effects using the web: A new approach tomedical hypothesis generation. J Biomed Informatics. 2011, 44: 989-996.
10. Roberts A, Gaizauskas R, Hepple M, Demetriou G, Guo Y, Roberts I, Setzer A: Building a semantically annotated corpus of clinical texts. J Biomed Informatics. 2009, 42: 950-966. 10.1016/j.jbi.2008.12.013.
11. Gurulingappa H, Mateen-Rajput A, Roberts A, Fluck J, Hofmann-Apitius M, Toldo L: Development of a Benchmark Corpus to Support the Automatic Extraction ofDrug-related Adverse Effects from Medical Case Reports. J Biomed Informatics. 2012, 45: 885-892. 10.1016/j.jbi.2012.04.008.
12. Hanisch D, Fundel K, Mevissen HT, Zimmer R, Fluck J: ProMiner: rule-based protein and gene entity recognition. BMC Bioinformatics. 2005, 6 (Suppl 1:S14): 10.1186/1471. [-2105-6-S1-S14]
13. Knox C, Law V, Jewison T, Liu P, Ly S, Frolkis A, Pon A, Banco K, Mak C, Neveu V, Djoumbou Y, Eisner R, Guo AC, Wishart DS: DrugBank 3.0: a comprehensive resource for 'omics' research ondrugs. Nucleic Acids Res. 2011, 39 (Database issue): D1035-D1041-10.1093/nar/gkq1126.
14. Merrill GH: The MedDRA paradox. Proceedings of the AMIA 2008 Annual Symposium. 2008, Washington, DC, USA, 470-474. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2655972/pdf/amia-0470-s2008.pdf
15. Giuliano C, Lavelli A, Pighin D, Romano L: FBK-IRST: Kernel Methods for Semantic Relation Extraction. Proceedings of the Fourth International Workshop on SemanticEvaluations. Edited by: Richard W, Lluís M, Agirre E, Lluís M, Richard W. 2007, Prague, Czech Republic, 141-144-141-144. http://aclweb.org/anthology-new/S/S07/S07-1000.pdf
16. Burges C: A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery. 1998, 2: 121-167-
17. Tikk D, Thomas P, Palaga P, Hakenberg J, Leser U: A comprehensive benchmark of kernel methods to extract protein-proteininteractions from literature. PLoS Comput Biol. 2010, 6: e1000837-10.1371/journal.pcbi.1000837.
18. Roberts A, Gaizauskas R, Hepple M, Demetriou G, Guo Y, Roberts I, Setzer A: The CLEF corpus: semantic annotation of clinical text. Proceedings of the AMIA Symposium. 2007, Chicago, IL, USA, 625-629. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2655900/pdf/amia-0625-s2007.pdf
19. Ogren P: Knowtator: a Protégé plug-in for annotated corpusconstruction. Proceedings of the 2006 conference of the North American chapter of theassociation for computational linguistics on human language technology. Edited by: Moore Robert C, Bilmes Jeff, Chu-Carroll Jennife, SandersonMark. 2006, New York, NY, USA, 273-275. http://aclweb.org/anthology-new/N/N06/N06-4006.pdf
20. Yongqun H, Zuoshuang X, Sarntivijai S, Toldo L, Ceusters W: AEO: A Realism-Based Biomedical Ontology for the Representation ofAdverse Events. "Representing Adverse Events" at the International Conference onBiomedical Ontology. Edited by: Courtot M, Goldfain A, Yongqun He O, Ruttenberg A. 2011, NY, USA: Buffalo, http://icbo.buffalo.edu/2011/workshop/adverse-events/docs/papers/HeAEICBO2011_submission.pdf
21. Kuhn M, Campillos M, Letunic I, Jensen LJ, Bork P: A side effect resource to capture phenotypic effects of drugs. Mol Syst Biol. 2010, 6: 343-10.1038/msb.2009.98.
22. van Mulligen E, Fourrier-Reglat A, Gurwitz D, Molokhia M, Nieto A, Trifiro G, Kors J, Furlong L: The EU-ADR Corpus: Annotated Drugs, Diseases, Targets, and theirRelationships. J Biomed Informatics. 2012, 45: 879-884. 10.1016/j.jbi.2012.04.004.
23. Toldo L, Gurulingappa H, Mateen-Rajput A, Kors J, Suri S, Tayrouz Y: Impact of Automatic Detection of Adverse Events on Prediction of Drug LabelChanges. J Pharmacoepidemiology and Drug Saf. 2012, [Submitted]
24. Wimalasuriya D, Dou D: Ontology-based information extraction: an introduction and a survey ofcurrent approaches. J Information Sci. 2010, 36: 306-323. 10.1177/0165551509360123.
25. Pandit S, Honavar V: Ontology-guided extraction of complex nested relationships. 22nd IEEE International Conference on tools with artificial intelligence(ICTAI). Edited by: Pierre M. 2010, France: Arras, 173-178. http://dx.doi.org/10.1109/ICTAI.2010.98