title: MITRE system for clinical assertion status classification.
abstract: OBJECTIVE
  To describe a system for determining the assertion status of medical problems mentioned in clinical reports, which was entered in the 2010 i2b2/VA community evaluation 'Challenges in natural language processing for clinical data' for the task of classifying assertions associated with problem concepts extracted from patient records.
  
  
  METHODS
  A combination of machine learning (conditional random field and maximum entropy) and rule-based (pattern matching) techniques was used to detect negation, speculation, and hypothetical and conditional information, as well as information associated with persons other than the patient.
  
  
  RESULTS
  The best submission obtained an overall micro-averaged F-score of 0.9343.
  
  
  CONCLUSIONS
  Using semantic attributes of concepts and information about document structure as features for statistical classification of assertions is a good way to leverage rule-based and statistical techniques. In this task, the choice of features may be more important than the choice of classifier algorithm.
authors.affiliation: The MITRE Corporation, Bedford, Massachusetts 01730-1420, USA. cclark@mitre.org
authors.forename: Cheryl
authors.initials: C
authors.lastname: Clark
authors.forename: John
authors.initials: J
authors.lastname: Aberdeen
authors.forename: Matt
authors.initials: M
authors.lastname: Coarr
authors.forename: David
authors.initials: D
authors.lastname: Tresner-Kirsch
authors.forename: Ben
authors.initials: B
authors.lastname: Wellner
authors.forename: Alexander
authors.initials: A
authors.lastname: Yeh
authors.forename: Lynette
authors.initials: L
authors.lastname: Hirschman
mesh_terms: D003463:Cues; D057225:Data Mining / Q000145:classification; D020000:Decision Support Systems, Clinical / Q000145:classification; D057286:Electronic Health Records / Q000145:classification; D006801:Humans; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000145:classification; D012660:Semantics; D035501:Uncertainty