title: A flexible framework for deriving assertions from electronic medical records.
abstract: OBJECTIVE
  This paper describes natural-language-processing techniques for two tasks: identification of medical concepts in clinical text, and classification of assertions, which indicate the existence, absence, or uncertainty of a medical problem. Because so many resources are available for processing clinical texts, there is interest in developing a framework in which features derived from these resources can be optimally selected for the two tasks of interest.
  
  
  METHODS
  The authors used two machine-learning (ML) classifiers: support vector machines (SVMs) and conditional random fields (CRFs). Because SVMs and CRFs can operate on a large set of features extracted from both clinical texts and external resources, the authors address the following research question: Which features need to be selected for obtaining optimal results? To this end, the authors devise feature-selection techniques which greatly reduce the amount of manual experimentation and improve performance.
  
  
  RESULTS
  The authors evaluated their approaches on the 2010 i2b2/VA challenge data. Concept extraction achieves 79.59 micro F-measure. Assertion classification achieves 93.94 micro F-measure.
  
  
  CONCLUSIONS
  Approaching medical concept extraction and assertion classification through ML-based techniques has the advantage of easily adapting to new data sets and new medical informatics tasks. However, ML-based techniques perform best when optimal features are selected. By devising promising feature-selection techniques, the authors obtain results that outperform the current state of the art.
  
  
  CONCLUSIONS
  This paper presents two ML-based approaches for processing language in the clinical texts evaluated in the 2010 i2b2/VA challenge. By using novel feature-selection methods, the techniques presented in this paper are unique among the i2b2 participants.
authors: Human Language Technology Research Institute, University of Texas at Dallas, Richardson, Texas 75080-0688, USA. kirk@hlt.utdallas.edu
authors: Kirk
authors: K
authors: Roberts
authors: Sanda M
authors: SM
authors: Harabagiu
mesh_terms: D057225:Data Mining / Q000145:classification; D020000:Decision Support Systems, Clinical / Q000145:classification; D057286:Electronic Health Records / Q000145:classification; D006801:Humans; D009323:Natural Language Processing; D012660:Semantics; D060388:Support Vector Machine; D035501:Uncertainty