title: Combining classifiers for robust PICO element detection.
abstract: BACKGROUND
  Formulating a clinical information need in terms of the four atomic parts which are Population/Problem, Intervention, Comparison and Outcome (known as PICO elements) facilitates searching for a precise answer within a large medical citation database. However, using PICO defined items in the information retrieval process requires a search engine to be able to detect and index PICO elements in the collection in order for the system to retrieve relevant documents.
  
  
  METHODS
  In this study, we tested multiple supervised classification algorithms and their combinations for detecting PICO elements within medical abstracts. Using the structural descriptors that are embedded in some medical abstracts, we have automatically gathered large training/testing data sets for each PICO element.
  
  
  RESULTS
  Combining multiple classifiers using a weighted linear combination of their prediction scores achieves promising results with an f-measure score of 86.3% for P, 67% for I and 56.6% for O.
  
  
  CONCLUSIONS
  Our experiments on the identification of PICO elements showed that the task is very challenging. Nevertheless, the performance achieved by our identification method is competitive with previously published results and shows that this task can be achieved with a high accuracy for the P element but lower ones for I and O elements.
authors.affiliation: DIRO, University of Montreal, CP. 6128, succursale Centre-ville, Montreal, H3C 3J7 Quebec, Canada. boudinfl@iro.umontreal.ca
authors.forename: Florian
authors.initials: F
authors.lastname: Boudin
authors.forename: Jian-Yun
authors.initials: JY
authors.lastname: Nie
authors.forename: Joan C
authors.initials: JC
authors.lastname: Bartlett
authors.forename: Roland
authors.initials: R
authors.lastname: Grad
authors.forename: Pierre
authors.initials: P
authors.lastname: Pluye
authors.forename: Martin
authors.initials: M
authors.lastname: Dawes
mesh_terms: D000043:Abstracting and Indexing / Q000145:classification*; D000465:Algorithms; D016206:Databases, Bibliographic; D016247:Information Storage and Retrieval / Q000379:methods*
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