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Lessons from the Drug-Drug Interaction Extraction Task
1. DDIExtraction 2011
Lessons from the Drug-Drug Interaction
Extraction Task
Huelva, September, 7th
Isabel Segura-Bedmar, Paloma Martinez, Daniel Sánchez-
Cisneros, LABDA, UC3M
http://labda.inf.uc3m.es/
2. Drug-Drug Interactions (DDIs)
A DDI occurs when one drug influences the
level or activity of another drug.
DDIs are a serious problem for patient safety.
Overwhelming amount of information available
on DDIs: databases, journals, technical reports,
books, etc.
Information Extraction (IE) can provide an
interesting way of reducing the time spent by
health care professionals on reviewing the
literature.
3. DDIExtraction Task
Automatic extraction of drug-drug
interactions from text
Main goals:
To produce useful resources for training
and testing.
To learn which approaches are
successful and practical.
To encourage in developing useful tools
to extract DDIs from texts.
5. Summary of Approaches
• Main approaches at the top level
– Markov
– Support Vector Machine (SVM)
• Typically with a small window around the word of interest
– Rules: typically manually generated?
• Also may have pre- or post-processing in
addition to the main approach
• Lots of different features used
– Some features need entire other sub-systems to
obtain a value
• E.g., a part-of-speech (POS) tagger
• The other sub-system may use an approach different
from the main one
6. Summary of Results: Comments
• Like many similar tasks, task 1A has its share of
unique aspects
– The 1-2 teams that did not really train against the
training data did not do as well
• Results for open and closed submissions are
typically fairly close
• At the higher scores
• Between the submissions from the same user
8. DDIExtraction 2011 Task
Pilot task.
Several limitations:
Drug names were automatically
annotated.
Annotation process performed by one
unique annotator.
Many annotation errors.
10. DDIExtraction Task in SEMEVAL
2013
New train and test datasets: unified format.
Identify drug-drug interaction pairs from full
text articles (HTML, PDF).
Drugs and drug-drug interactions manually
annotated by two different pharmacists.
Calculate Inter-annotator agreement (IAA).
11. DDIExtraction Task in SEMEVAL
2013
September 7, 2011 Call for participation
April 10, 2012 Full Training Data available for
participants
January 1, 2013 Start of evaluation period
February 1, 2013 End of Evaluation Period
March 1, 2013 Paper submission deadline
Summer 2013 Workshop co-located with ACL or
NAACL
12. Acknowledgments
DrugBank for providing the training and test
data collections.
Manuel Maña for collaboration in organizing the
DDIExtraction task.
Thanks to the PC members for their invaluable
help.
Thanks to the participating teams for their effort
in developing the participating systems and
improving the quality of the datasets.
DDIs are a serious problem for patient safety since these interactions can become very dangerous and increase health care costs.
There are many different sources of information on DDIs such as databases, journals, technical reports, books, making the medical literature the most effective source for the deetection of DDIs.
Information Extraction (IE) can be of great benefit in the pharmaceutical industry allowing identification and extraction of relevant information on DDI and providing an interesting way of reducing the time spent by health care professionals on reviewing
the literature.
Annotate additional information about each interaction: roles of each drug, mechanism, doses of both drugs, its time course, seriousness, severity, probability.
Identify the roles of the interacting drugs.
Identify the doses of both drugs.
Identify the time course.
Identify the seriousness, severity, probability.