Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on medication adherence

LIVE: Thursday 27th June 2024, 18:00-19:30 (CEST)
Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.
ADVANCED SEARCH
Log in
Top
BMC Medical Informatics and Decision Making
Published in: BMC Medical Informatics and Decision Making 1/2014

Open Access 01-12-2014 | Research article

A pipeline to extract drug-adverse event pairs from multiple data sources

Authors: SriJyothsna Yeleswarapu, Aditya Rao, Thomas Joseph, Vangala Govindakrishnan Saipradeep, Rajgopal Srinivasan

Published in: BMC Medical Informatics and Decision Making | Issue 1/2014

Login to get access

Abstract

Background

Pharmacovigilance aims to uncover and understand harmful side-effects of drugs, termed adverse events (AEs). Although the current process of pharmacovigilance is very systematic, the increasing amount of information available in specialized health-related websites as well as the exponential growth in medical literature presents a unique opportunity to supplement traditional adverse event gathering mechanisms with new-age ones.

Method

We present a semi-automated pipeline to extract associations between drugs and side effects from traditional structured adverse event databases, enhanced by potential drug-adverse event pairs mined from user-comments from health-related websites and MEDLINE abstracts. The pipeline was tested using a set of 12 drugs representative of two previous studies of adverse event extraction from health-related websites and MEDLINE abstracts.

Results

Testing the pipeline shows that mining non-traditional sources helps substantiate the adverse event databases. The non-traditional sources not only contain the known AEs, but also suggest some unreported AEs for drugs which can then be analyzed further.

Conclusion

A semi-automated pipeline to extract the AE pairs from adverse event databases as well as potential AE pairs from non-traditional sources such as text from MEDLINE abstracts and user-comments from health-related websites is presented.
Appendix
Available only for authorised users
Literature
1.
Wang X, Hripcsak G, Markatou M, Friedman C: Active computerized pharmacovigilance using natural language processing, statistics, and electronic health records: a feasibility study. JAMIA. 2009, 16: 328-337.PubMedPubMedCentral
2.
World Health Organization: The importance of pharmacovigilance- Safety Monitoring of Medicinal Products. 2002, Geneva: World Health Organization
3.
Wang W, Haerian K, Salmasian H, Harpaz R, Chase H, Friedman C: A drug-adverse event extraction algorithm to support pharmacovigilance knowledge mining from PubMed citations. AMIA Annual Symposium Proceedings. 2011, 1464-1470.
4.
Holmes AB, Hawson A, Liu F, Friedman C, Khiabanian H, Rabadan R: Discovering disease associations by integrating electronic clinical data and medical literature. PLoS One. 2011, 6: e21132-10.1371/journal.pone.0021132.CrossRefPubMedPubMedCentral
5.
6.
Harpaz R, Haerian K, Chase HS, Friedman C: Statistical Mining of Potential Drug Interaction Adverse Effects in FDA’s Spontaneous Reporting System. AMIA Annual Symposium Proceedings. 2010, 281-285.
7.
8.
Coloma P, Schuemie MJ, Trifirò G, Gini R, Herings R, Hippisley-Cox J, Mazzaglia G, Giaquinto C, Corrao G, Pedersen L, van der Lei J, Sturkenboom M, on behalf of the EU-ADR Consortium: Combining electronic healthcare databases in Europe to allow for large-scale drug safety monitoring: the EU-ADR Project. Pharmacoepidemiol Drug Saf. 2011, 20: 1-11. 10.1002/pds.2053.CrossRefPubMed
9.
Trifirò G, Pariente A, Coloma PM, Kors JA, Polimeni G, Miremont-Salamé G, Catania MA, Salvo F, David A, Moore N, Caputi AP, Sturkenboom M, Molokhia M, Hippisley-Cox J, Acedo CD, van der Lei J, Fourrier-Reglat M, EU-ADR group: Data mining on electronic health record databases for signal detection in pharmacovigilance: which events to monitor?. Pharmacoepidemiol Drug Saf. 2009, 18: 1176-1184. 10.1002/pds.1836.CrossRefPubMed
10.
Ryan PB, Madigan D, Stang PE, Overhage JM, Racoosin JA, Hartzema AG: Empirical assessment of methods for risk identification in healthcare data: results from the experiments of the observational medical outcomes partnership. Statist Med. 2012, 31: 4401-4415. 10.1002/sim.5620.CrossRef
11.
Curtis LH, Weiner MG, Boudreau DM, Cooper WO, Daniel GW, Nair VP, Raebel MA, Beaulieu NU, Rosofsky R, Woodworth TS, Brown JS: Design considerations, architecture, and use of the Mini-Sentinel distributed data system. Pharmacoepidemiol Drug Saf. 2012, 21: 23-31.CrossRefPubMed
12.
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 on drugs. Nucleic Acids Res. 2011, 39 (Database issue): D1035-D1041.CrossRefPubMed
13.
Cimino JJ: Representation of clinical laboratory terminology in the Unified Medical Language System. Proc Annu Symp Comput Appl Med Care. 1991, 199-203.
14.
Fiszman M, Rindflesch TC, Kilicoglu H: Abstraction summarization for managing the biomedical research literature. Proc Workshop Comp Lexical Semantics. 2004, 76-83. HLT-NAACL
15.
Wang X, Chase HS, Li J, Hripcsak G, Friedman C: Integrating heterogeneous knowledge sources to acquire executable drug-related knowledge. AMIA Annual Symposium Proceedings. 2010, 852-856.
16.
Gurulingappa H, Rajput AM, 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: 885-892. 10.1016/j.jbi.2012.04.008.CrossRefPubMed
17.
Gurulingappa H, Mateen-Rajput A, Toldo L: Extraction of potential adverse drug events from medical case reports. J Biomed Semantics. 2012, 3: 15-10.1186/2041-1480-3-15.CrossRefPubMedPubMedCentral
18.
van Mulligen EM, Fourrier-Reglat A, Gurwitz D, Molokhia M, Nieto A, Trifiro G, Kors JA, Furlong LI: The EU-ADR corpus: annotated drugs, diseases, targets, and their relationships. J Biomed Inform. 2012, 45: 879-884. 10.1016/j.jbi.2012.04.004.CrossRefPubMed
19.
20.
Leaman R, Wojtulewicz L, Sullivan R, Skariah A, Yang J, Gonzalez G: Towards Internet-Age Pharmacovigilance: Extracting Adverse Drug Reactions from User Posts to Health-Related Social Networks. 2010, Uppsala, Sweden ACL: Proceedings of the 2010 Workshop on Biomedical Natural Language Processing, 117-125. 7
21.
22.
23.
Ackroyd-Stolarz SA, MacKinnon NJ, Murphy N, Gillespie E, Zed PJ: Adverse events related to medications identified by a Canadian poison centre. J Popul Ther Clin Pharmacol. 2011, 18: e250-e256.PubMed
24.
Joseph T, Saipradeep VG, Raghavan GS, Srinivasan R, Rao A, Kotte S, Sivadasan N: TPX: biomedical literature search made easy. Bioinformation. 2012, 8: 578-580. 10.6026/97320630008578.CrossRefPubMedPubMedCentral
25.
Fielding RT, Taylor RN: Principled Design of the Modern Web Architecture. ACM Trans Internet Technol. 2002, 2: 115-150. 10.1145/514183.514185.CrossRef
26.
27.
Smith L, Rindflesch T, Wilbur WJ: MedPost: a part-of-speech tagger for bioMedical text. Bioinformatics. 2004, 20: 2320-2321. 10.1093/bioinformatics/bth227.CrossRefPubMed
28.
Porter MF: An algorithm for suffix stripping. Program. 1980, 14: 130-137. 10.1108/eb046814.CrossRef
29.
30.
31.
32.
Bate A: Bayesian confidence propagation neural network. Drug Saf. 2007, 30: 623-625. 10.2165/00002018-200730070-00011.CrossRefPubMed
33.
Bate A, Lindquist M, Edwards IR, Olsson S, Orre R, Lansner A, De Freitas RM: A Bayesian neural network method for adverse drug reaction signal generation. Eur J Clin Pharmacol. 1998, 54: 315-321. 10.1007/s002280050466.CrossRefPubMed
34.
35.
36.
37.
38.
Denecke K, Nejdl W: How valuable is medical social media data? Content analysis of the medical web. Inf Sci. 2009, 179: 1870-1880. 10.1016/j.ins.2009.01.025.CrossRef
39.
Shah SG, Robinson I: Patients’ perspectives on self-testing of oral anticoagulation therapy: content analysis of patients’ internet blogs. BMC Health Serv Res. 2011, 11: 25-10.1186/1472-6963-11-25.CrossRefPubMedPubMedCentral
40.
41.
Sherrod RA, Ford C, Oliver J: Using data from the internet to teach ethical principles for critiquing research studies. Nurse Educ. 2010, 35: 17-19. 10.1097/NNE.0b013e3181c41f8d.CrossRefPubMed
42.
Whitehead LC: Methodological and ethical issues in Internet-mediated research in the field of health: an integrated review of the literature. Soc Sci Med. 2007, 65: 782-791. 10.1016/j.socscimed.2007.03.005.CrossRefPubMed
43.
44.
Szarfman A, Tonning JM, Doraiswamy PM: Pharmacovigilance in the 21st century: new systematic tools for an old problem. Pharmacotherapy. 2004, 24: 1099-1104. 10.1592/phco.24.13.1099.38090.CrossRefPubMed
45.
Evans SJW, Waller PC, Davis S: Use of proportional reporting ratios (PRRs) for signal generation from spontaneous adverse drug reaction reports. Pharmacoepidemiol Drug Saf. 2001, 6: 483-486.CrossRef
46.
van Puijenbroek EP, Egberts AC, Meyboom RH, Leufkens HG: Signalling possible drug–drug interactions in a spontaneous reporting system: delay of withdrawal bleeding during concomitant use of oral contraceptives and itraconazole. Br J Clin Pharmacol. 1999, 47: 689-693.CrossRefPubMedPubMedCentral
47.
Lindquist M, Edwards IR, Bate A, Fucik H, Nunes AM, Stahl M: From association to alert–a revised approach to international signal analysis. Pharmacoepidemiol Drug Safety. 1999, 1: 15-25.CrossRef
48.
Matsushita Y, Kuroda Y, Niwa S, Sonehara S, Hamada C, Yoshimura I: Criteria revision and performance comparison of three methods of signal detection applied to the spontaneous reporting database of a pharmaceutical manufacturer. Drug Saf. 2007, 30: 715-726. 10.2165/00002018-200730080-00008.CrossRefPubMed
Metadata
Title
A pipeline to extract drug-adverse event pairs from multiple data sources
Authors
SriJyothsna Yeleswarapu
Aditya Rao
Thomas Joseph
Vangala Govindakrishnan Saipradeep
Rajgopal Srinivasan
Publication date
01-12-2014
Publisher
BioMed Central
Published in
BMC Medical Informatics and Decision Making / Issue 1/2014
Electronic ISSN: 1472-6947
DOI
https://doi.org/10.1186/1472-6947-14-13

Other articles of this Issue 1/2014

BMC Medical Informatics and Decision Making 1/2014 Go to the issue

Research article

Development of a personalized decision aid for breast cancer risk reduction and management

Research article

Automated comparison of last hospital main diagnosis and underlying cause of death ICD10 codes, France, 2008–2009

Research article

A systematic review of the implementation and impact of asthma protocols

Research article

Diagnostic accuracy of a screening electronic alert tool for severe sepsis and septic shock in the emergency department

Research article

Improving access to clinical practice guidelines with an interactive graphical interface using an iconic language

Research article

Real-time automatic hospital-wide surveillance of nosocomial infections and outbreaks in a large Chinese tertiary hospital