title: Classification of Clinically Useful Sentences in MEDLINE.
abstract: In a previous study, we investigated a sentence classification model that uses semantic features to extract clinically useful sentences from UpToDate, a synthesized clinical evidence resource. In the present study, we assess the generalizability of the sentence classifier to Medline abstracts.
  
  
  
  We applied the classification model to an independent gold standard of high quality clinical studies from Medline. Then, the classifier trained on UpToDate sentences was optimized by re-retraining the classifier with Medline abstracts and adding a sentence location feature.
  
  
  
  The previous classifier yielded an F-measure of 58% on Medline versus 67% on UpToDate. Re-training the classifier on Medline improved F-measure to 68%; and to 76% (p<0.01) after adding the sentence location feature.
  
  
  
  The classifier's model and input features generalized to Medline abstracts, but the classifier needed to be retrained on Medline to achieve equivalent performance. Sentence location provided additional contribution to the overall classification performance.
authors.affiliation: Department of Operations and Information Systems, David Eccles School of Business, University of Utah, Salt Lake City, UT, USA.
authors.forename: Mohammad Amin
authors.initials: MA
authors.lastname: Morid
authors.affiliation: Department of Preventive Medicine, Division of Health and Biomedical Informatics, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
authors.forename: Siddhartha
authors.initials: S
authors.lastname: Jonnalagadda
authors.affiliation: Lister Hill Center, National Library of Medicine, Bethesda, MD, USA.
authors.forename: Marcelo
authors.initials: M
authors.lastname: Fiszman
authors.affiliation: Department of Preventive Medicine, Division of Health and Biomedical Informatics, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
authors.forename: Kalpana
authors.initials: K
authors.lastname: Raja
authors.affiliation: Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA.
authors.forename: Guilherme
authors.initials: G
authors.lastname: Del Fiol
mesh_terms: D006801:Humans; D016239:MEDLINE; D000069550:Machine Learning; D012660:Semantics