title: A flexible framework for recognizing events, temporal expressions, and temporal relations in clinical text.
abstract: OBJECTIVE
  To provide a natural language processing method for the automatic recognition of events, temporal expressions, and temporal relations in clinical records.
  
  
  METHODS
  A combination of supervised, unsupervised, and rule-based methods were used. Supervised methods include conditional random fields and support vector machines. A flexible automated feature selection technique was used to select the best subset of features for each supervised task. Unsupervised methods include Brown clustering on several corpora, which result in our method being considered semisupervised.
  
  
  RESULTS
  On the 2012 Informatics for Integrating Biology and the Bedside (i2b2) shared task data, we achieved an overall event F1-measure of 0.8045, an overall temporal expression F1-measure of 0.6154, an overall temporal link detection F1-measure of 0.5594, and an end-to-end temporal link detection F1-measure of 0.5258. The most competitive system was our event recognition method, which ranked third out of the 14 participants in the event task.
  
  
  CONCLUSIONS
  Analysis reveals the event recognition method has difficulty determining which modifiers to include/exclude in the event span. The temporal expression recognition method requires significantly more normalization rules, although many of these rules apply only to a small number of cases. Finally, the temporal relation recognition method requires more advanced medical knowledge and could be improved by separating the single discourse relation classifier into multiple, more targeted component classifiers.
  
  
  CONCLUSIONS
  Recognizing events and temporal expressions can be achieved accurately by combining supervised and unsupervised methods, even when only minimal medical knowledge is available. Temporal normalization and temporal relation recognition, however, are far more dependent on the modeling of medical knowledge.
authors.affiliation: Human Language Technology Research Institute, The University of Texas at Dallas, Richardson, Texas, USA.
authors.forename: Kirk
authors.initials: K
authors.lastname: Roberts
authors.forename: Bryan
authors.initials: B
authors.lastname: Rink
authors.forename: Sanda M
authors.initials: SM
authors.lastname: Harabagiu
mesh_terms: D001185:Artificial Intelligence; D057286:Electronic Health Records; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D009323:Natural Language Processing; D013995:Time; D057170:Translational Research, Biomedical
keywords: Clinical Informatics; Medical Records Systems, Computerized; Natural Language Processing