title: Leveraging medical thesauri and physician feedback for improving medical literature retrieval for case queries.
abstract: OBJECTIVE
  This paper presents a study of methods for medical literature retrieval for case queries, in which the goal is to retrieve literature articles similar to a given patient case. In particular, it focuses on analyzing the performance of state-of-the-art general retrieval methods and improving them by the use of medical thesauri and physician feedback.
  
  
  METHODS
  The Kullback-Leibler divergence retrieval model with Dirichlet smoothing is used as the state-of-the-art general retrieval method. Pseudorelevance feedback and term weighing methods are proposed by leveraging MeSH and UMLS thesauri. Evaluation is performed on a test collection recently created for the ImageCLEF medical case retrieval challenge.
  
  
  RESULTS
  Experimental results show that a well-tuned state-of-the-art general retrieval model achieves a mean average precision of 0.2754, but the performance can be improved by over 40% to 0.3980, through the proposed methods.
  
  
  CONCLUSIONS
  The results over the ImageCLEF test collection, which is currently the best collection available for the task, are encouraging. There are, however, limitations due to small evaluation set size. The analysis shows that further refinement of the methods is necessary before they can be really useful in a clinical setting.
  
  
  CONCLUSIONS
  Medical case-based literature retrieval is a critical search application that presents a number of unique challenges. This analysis shows that the state-of-the-art general retrieval models are reasonably good for the task, but the performance can be significantly improved by developing new task-specific retrieval models that incorporate medical thesauri and physician feedback.
authors.affiliation: Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801-2302, USA. sondhi1@illinois.edu
authors.forename: Parikshit
authors.initials: P
authors.lastname: Sondhi
authors.forename: Jimeng
authors.initials: J
authors.lastname: Sun
authors.forename: ChengXiang
authors.initials: C
authors.lastname: Zhai
authors.forename: Robert
authors.initials: R
authors.lastname: Sorrentino
authors.forename: Martin S
authors.initials: MS
authors.lastname: Kohn
mesh_terms: D000465:Algorithms; D005246:Feedback; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D046650:Medical Subject Headings; D009323:Natural Language Processing; D010820:Physicians; D017432:Unified Medical Language System; D014584:User-Computer Interface