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DEADLINE PASSED! - See the ICBO website for more information

First International Workshop on

Drug Interaction Knowledge Representation

October 6th and 7th, 2014
Houston, Texas, United States

In conjunction with

Fifth International Conference on Biomedical Ontologies

Call for Papers

The combination of poor quality evidence and a general lack of drug-drug interaction (DDI) knowledge by persons who prescribe drugs results in many thousands of preventable medication errors each year. While many sources of DDI evidence exist to help improve prescriber knowledge, no clinician-oriented meta-data standard currently exists. Such a standard could enable a more effective synthesis of DDI evidence during tasks such as consulting and guideline development.

The goal of this workshop is to bring clinical and ontology experts together to discuss

a) potential DDI knowledge representation solutions that reflect the state-of-the-art of both the clinical understanding of DDIs and biomedical ontology development,

b) how to best link DDI ontologies to pre-existing drug terminology efforts, and

c) roadblocks to the adoption of ontology-driven solutions such as coverage, usability, and scalability.

Areas of Interest to the Workshop

  • Previous and current drug-drug interaction ontology development efforts
  • Competency questions and clinical, epidemiologic, and translational use cases
  • Approaches to representing drug-drug interaction evidence and scientific discourse 
  • Drug-drug interaction ontology maintenance
  • How to move toward an international standard for representing and sharing drug-drug interaction knowledge

Venue and Format

The authors of accepted papers will present 15 minute talks followed by brief discussions. Following papers presentations, an interdisciplinary panel will lead a moderated discussion focusing on prioritizing research challenges. All workshop participants will provide input and help guide the discussion using a live survey system. Accepted papers and a summary of the moderated discussion will be published in the proceedings and made available at CEUR (http://ceur-ws.org/).

Intended Audience

  • Anyone with an interest in how information systems can best represent and share drug-drug interaction knowledge for clinically oriented applications. These might include:
    • Clinical and translational scientists who focus on drug safety
    • Terminology and ontology developers or users
    • Clinical decision support developers or users
    • Natural Language Processing researchers
    • Drug compendium developers or editors
    • Regulatory scientists

Important Dates

EXTENDED Paper Submission deadline: August 11, 2014 --- PASSED!!

Notification of paper acceptance: August 31, 2014

Paper Submission

Papers describing original research or novel applications are welcome. All papers  be peer reviewed to assess the quality of scientific method and potential for the work to advance DDI knowledge representation beyond existing methods or technologies. 

Papers should be between 5 - 10 pages long excluding references. Please use Springer's Lecture Notes in Computer Science (LNCS) templates:

Springer Author Guidelines (pdf): Click Here

Springer LNCS template files: Click Here

Submit manuscripts to the Easy Chair submission site: Click Here


Richard D. Boyce (University of Pittsburgh)
Mathias Brochhausen (University of Arkansas for Medical Sciences)
Philip Empey (University of Pittsburgh)
William R. Hogan (University of Arkansas for Medical Sciences)
Daniel Malone (University of Arizona)

Scientific Committee

Richard D. Boyce (University of Pittsburgh)
Mathias Brochhausen (University of Arkansas for Medical Sciences)
Michel Dumontier (Stanford University)
Jon Duke (Regenstrief Institute)
Philip Empey (University of Pittsburgh)
William R. Hogan (University of Arkansas for Medical Sciences)
Daniel Malone (University of Arizona)
Alan Ruttenberg (University at Buffalo)
David Weinstein (Wolters Kluwer Health)

Supported By:

This project is supported by a grant from the National Library of Medicine: "Addressing gaps in clinically useful evidence on drug-drug interactions" (1R01LM011838-01)