Pharmacovigilance through the development of text mining and natural language processing techniques

Journal of Biomedical Informatics

Volumn 58, Issue , 2015, Pages 288-291

  Segura Bedmar, Isabel ^a   Martínez, Paloma ^a  

^a UNIVERSIDAD CARLOS III DE MADRID   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; CLINICAL PROTOCOL; DATA EXTRACTION; DATA MINING; DRUG MANUFACTURE; DRUG SAFETY; DRUG SURVEILLANCE PROGRAM; EDITORIAL; HEALTH CARE COST; HOSPITAL ADMISSION; HUMAN; NATURAL LANGUAGE PROCESSING; PATIENT SAFETY; PRESCRIPTION; PRIORITY JOURNAL; RESOURCE ALLOCATION; SOCIAL MEDIA;

DATA MINING; NATURAL LANGUAGE PROCESSING; PHARMACOVIGILANCE;

EID: 84949322456     PISSN: 15320464     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jbi.2015.11.001     Document Type: Editorial

References (23)

1. Edwards I.R., Aronson J.K. Adverse drug reactions: definitions, diagnosis, and management. The Lancet 2000, 356(9237):1255-1259.
2. US Department of Health and Human Services, Office of Disease Prevention and Health Promotion, National Action Plan for Adverse Drug Event Prevention, Washington, DC, 2014.
3. McClellan M. Drug safety reform at the FDA-pendulum swing or systematic improvement?. N. Engl. J. Med. 2007, 356(17):1700-1702.
4. I. Segura Bedmar, P. Martinez, D. Sánchez Cisneros, The 1st DDIExtraction-2011 challenge task: extraction of drug-drug interactions from biomedical texts, in: Proceedings of the First DDIExtraction-2011 challenge task, 2011, pp. 1-9.
5. Segura-Bedmar I., Martínez P., Herrero-Zazo M. Lessons learnt from the DDIExtraction-2013 shared task. J. Biomed. Inform. 2014, 51:152-164.
6. M. Krallinger, O. Rabal, A. Lourenço, M.P. Perez, G.P. Rodriguez, M. Vazquez, F. Leitner, J. Oyarzabal, A. Valencia, Overview of the CHEMDNER patents task, in: Proceedings of the Fifth BioCreative Challenge Evaluation Workshop, 2015, pp. 63-75.
7. Wei C.H., Peng Y., Leaman R., Davis A.P., Mattingly C.J., Li J., Wiegers T.C., Lu Z. Overview of the BioCreative V chemical disease relation (CDR) task. Proc. Fifth BioCreative Challenge Eval. Workshop 2015, 154-166.
8. Oronoz M., Gojenola K., Pérez A., de Ilarraza A.D., Casillas A. On the creation of a clinical gold standard corpus in Spanish: mining adverse drug reactions. J. Biomed. Inform. 2015, 56:318-332.
9. Open Access, Collaborative Consumer Health Vocabulary Initiative [Internet], University of Utah, Biomedical Informatics Department. <> (cited 26.10.15). http://consumerhealthvocab.org/.
10. Karimi S., Metke-Jimenez A., Kemp M., Wang C. CADEC: a corpus of adverse drug event annotations. J. Biomed. Inform. 2015, 55:73-81.
11. Ayvaz S., Horn J., Hassanzadeh O., Zhu Q., Stan J., Tatonetti N.P., Vilar S., Brochhausen M., Samwald M., Rastegar-Mojarad M., Boyce R.D. Toward a complete dataset of drug-drug interaction information from publicly available sources. J. Biomed. Inform. 2015, 55:206-217.
12. Yang M., Kiang M., Shang W. Filtering big data from social media-building an early warning system for adverse drug reactions. J. Biomed. Inform. 2015, 54:230-240.
13. Alvaro N., Conway M., Doan S., Lofi C., Overington J., Collier N. Crowdsourcing Twitter annotations to identify first-hand experiences of prescription drug effects. J. Biomed. Inform. 2015, 58:280-287.
14. Liu X., Chen H. A research framework for pharmacovigilance in health social media: identification and evaluation of patient adverse drug event reports. J. Biomed. Inform. 2015, 58:268-279.
15. Winnenburg R., Sorbello A., Ripple A., Harpaz R., Tonning J., Szarfman A., Francis H., Bodenreider O. Leveraging MEDLINE indexing for pharmacovigilance-inherent limitations and mitigation strategies. J. Biomed. Inform. 2015, 58:425-435.
16. Xu R., Wang Q. Large-scale automatic extraction of side effects associated with targeted anticancer drugs from full-text oncological articles. J. Biomed. Inform. 2015, 55:64-72.
17. Kim S., Liu H., Yeganova L., Wilbur W.J. Extracting drug-drug interactions from literature using a rich feature-based linear kernel approach. J. Biomed. Inform. 2015, 55:23-30.
18. Herrero-Zazo M., Segura-Bedmar I., Martínez P., Declerck T. The DDI corpus: an annotated corpus with pharmacological substances and drug-drug interactions. J. Biomed. Inform. 2013, 46(5):914-920.
19. Abacha A.B., Chowdhury M.F.M., Karanasiou A., Mrabet Y., Lavelli A., Zweigenbaum P. Text mining for pharmacovigilance: using machine learning for drug name recognition and drug-drug interaction extraction and classification. J. Biomed. Inform. 2015, 58:122-132.
20. Hamed A.A., Wu X., Erickson R., Fandy T. Twitter KH networks in action: advancing biomedical literature for drug search. J. Biomed. Inform. 2015, 56:157-168.
21. Noferesti S., Shamsfard M. Using linked data for polarity classification of patients' experiences. J. Biomed. Inform. 2015, 57:6-19.
22. Sarker A., Ginn R., Nikfarjam A., O'Connor K., Smith K., Jayaraman S., Upadhaya T., Gonzalez G. Utilizing social media data for pharmacovigilance: a review. J. Biomed. Inform. 2015, 54:202-212.
23. Herrero-Zazo M., Segura-Bedmar I., Hastings J., Martínez P. DINTO: using OWL ontologies and SWRL rules to infer drug-drug interactions and their mechanisms. J. Chem. Inf. Model. 2015, 55(8):1698-1707.