title: A model based on multi-features to enhance healthcare and medical document retrieval.
abstract: OBJECTIVE
  A major problem in biomedical informatics is the contextual retrieval and ranking of medical and healthcare information. In this article, we present a model for extracting semantic relations among medical and clinical documents. The purpose is to maximise contextual retrieval and ranking performance with minimum input from users.
  
  
  METHODS
  We developed and evaluated a medical search engine that relies on a multi-features similarity model. The indexed documents are represented as a network that reflects the semantic relations among documents to assess topical rankings.
  
  
  METHODS
  The evaluation measurements include the following: recall, precision and R-precision. We used OHSUMED collection to evaluate our work with runs submitted to TREC-9. We provide a comparison of the top five runs that achieved the highest average precision scores. In addition, we used questionnaire-based evaluation to measure the effectiveness of the ranking task.
  
  
  RESULTS
  The results indicated that the proposed model achieved a higher average precision in comparison with top-scored runs submitted to TREC-9; the improvement of our model over other methods is statistically significant (p-value <0.0001). Furthermore, a questionnaire-based experiment showed that the proposed model performed quite well in ranking retrieved documents according to their topics.
authors.affiliation: Department of Information Systems, Informatics Institute, Middle East Technical University, İnönü Buvari,06531 Ankara, Turkey. e152255@metu.edu.tr
authors.forename: Mohammed G H
authors.initials: MG
authors.lastname: Al Zamil
authors.forename: Aysu
authors.initials: A
authors.lastname: Betin Can
mesh_terms: D057286:Electronic Health Records; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D057386:Search Engine / Q000379:methods; D012985:Software Design