PMID- 24559132
OWN - NLM
STAT- MEDLINE
DCOM- 20141021
LR  - 20220318
IS  - 1472-6947 (Electronic)
IS  - 1472-6947 (Linking)
VI  - 14
DP  - 2014 Feb 24
TI  - A pipeline to extract drug-adverse event pairs from multiple data sources.
PG  - 13
LID - 10.1186/1472-6947-14-13 [doi]
AB  - 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.
FAU - Yeleswarapu, Srijyothsna
AU  - Yeleswarapu S
FAU - Rao, Aditya
AU  - Rao A
AD  - TCS Innovation Labs, Tata Consultancy Services Ltd, Deccan Park, 1, Software 
      Units Layout, Madhapur, Hyderabad 500081, Andhra Pradesh, India. 
      adityar.rao@tcs.com.
FAU - Joseph, Thomas
AU  - Joseph T
FAU - Saipradeep, Vangala Govindakrishnan
AU  - Saipradeep VG
FAU - Srinivasan, Rajgopal
AU  - Srinivasan R
LA  - eng
PT  - Journal Article
DEP - 20140224
PL  - England
TA  - BMC Med Inform Decis Mak
JT  - BMC medical informatics and decision making
JID - 101088682
SB  - IM
MH  - Adverse Drug Reaction Reporting Systems/*standards
MH  - *Algorithms
MH  - Data Mining/*methods
MH  - *Drug-Related Side Effects and Adverse Reactions
MH  - Humans
MH  - Natural Language Processing
PMC - PMC3936866
EDAT- 2014/02/25 06:00
MHDA- 2014/10/22 06:00
PMCR- 2014/02/24
CRDT- 2014/02/25 06:00
PHST- 2013/06/11 00:00 [received]
PHST- 2014/02/14 00:00 [accepted]
PHST- 2014/02/25 06:00 [entrez]
PHST- 2014/02/25 06:00 [pubmed]
PHST- 2014/10/22 06:00 [medline]
PHST- 2014/02/24 00:00 [pmc-release]
AID - 1472-6947-14-13 [pii]
AID - 10.1186/1472-6947-14-13 [doi]
PST - epublish
SO  - BMC Med Inform Decis Mak. 2014 Feb 24;14:13. doi: 10.1186/1472-6947-14-13.