title: 2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text.
abstract: The 2010 i2b2/VA Workshop on Natural Language Processing Challenges for Clinical Records presented three tasks: a concept extraction task focused on the extraction of medical concepts from patient reports; an assertion classification task focused on assigning assertion types for medical problem concepts; and a relation classification task focused on assigning relation types that hold between medical problems, tests, and treatments. i2b2 and the VA provided an annotated reference standard corpus for the three tasks. Using this reference standard, 22 systems were developed for concept extraction, 21 for assertion classification, and 16 for relation classification. These systems showed that machine learning approaches could be augmented with rule-based systems to determine concepts, assertions, and relations. Depending on the task, the rule-based systems can either provide input for machine learning or post-process the output of machine learning. Ensembles of classifiers, information from unlabeled data, and external knowledge sources can help when the training data are inadequate.
authors.affiliation: Department of Information Studies, University at Albany, State University of New York, Albany, New York 12222, USA. ouzuner@albany.edu
authors.forename: Özlem
authors.initials: Ö
authors.lastname: Uzuner
authors.forename: Brett R
authors.initials: BR
authors.lastname: South
authors.forename: Shuying
authors.initials: S
authors.lastname: Shen
authors.forename: Scott L
authors.initials: SL
authors.lastname: DuVall
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