The Impact of Belief Values on the Identification of Patient Cohorts

  • Travis Goodwin
  • Sanda M. Harabagiu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8138)


Retrieving relevant patient cohorts has the potential to accelerate clinical research. Recent evaluations have shown promising results, but also relevance measures that still need to be improved. To address the challenge of better modelling hospital visit relevance, we considered the impact of two forms of medical knowledge on the quality of patient cohorts. First, we automatically identified three types of medical concepts and, second, we asserted their belief values. This allowed us to perform experiments that capture the impact of incorporating knowledge of belief values within a retrieval system for identifying hospital visits corresponding to patient cohorts. We show that this approach generates a 149% increase for inferred average precision, a 36.5% increase of NDCG, and a 207% increase to the precision of the first ten returned documents.


Electronic Medical Record Atypical Antipsychotic Hospital Visit Query Expansion Medical Concept 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Travis Goodwin
    • 1
  • Sanda M. Harabagiu
    • 1
  1. 1.Human Language Technology Research InstituteUniversity of Texas at DallasRichardsonUSA

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