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Title page for ETD etd-11302005-134810

Type of Document Master's Thesis
Author Duda, Stephany Norah
URN etd-11302005-134810
Title Developing Computer-generated PubMed Queries for Identifying Drug-Drug Interaction Content in MEDLINE
Degree Master of Science
Department Biomedical Informatics
Advisory Committee
Advisor Name Title
Kevin B. Johnson Committee Chair
Constantin F. Aliferis Committee Member
Randolph A. Miller Committee Member
  • information retrieval
  • cui
  • machine learning
  • svm
  • mmtx
  • metamap
  • Drug interactions -- Prevention -- Information resources
  • Electronic information resource searching
Date of Defense 2005-11-21
Availability unrestricted
Unwanted drug-drug interactions endanger millions of patients each year and burden families and the hospital system with escalating costs. Computer-based alerting systems are designed to prevent these interactions, yet the knowledge bases that support these systems often contain incomplete, clinically insignificant, and inaccurate drug information that can contribute to false alerts and wasted time. It may be possible to improve the content of these drug interaction databases by facilitating access to new or underused sources of drug-drug interaction information. The National Library of Medicine's MEDLINE database represents a respected source of peer-reviewed biomedical citations that would serve as a valuable source of information if the relevant articles could be pinpointed effectively and efficiently. This research compared the classification capabilities of human-generated and computer-generated Boolean queries as methods for locating articles about drug interactions. Two manual queries were assembled by medical librarians specializing in MEDLINE searches, and three computer-based queries were developed using a decision tree modeled on Support Vector Machine output. All five queries were tested on a corpus of manually-labeled positive and negative drug-drug interaction citations. Overall, the study showed that computer-generated queries derived from automated classification techniques have the potential to perform at least as well as manual queries in identifying drug-drug interaction articles in MEDLINE.
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