2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text

作者：Özlem Uzuner, Brett R. South, Shuying Shen, Scott L. DuVall · 发表于：Journal of the American Medical Informatics Association · 年份：2011 · DOI：10.1136/amiajnl-2011-000203 · 被引用次数：1291 · 研究领域：Biomedical Text Mining and Ontologies、Topic Modeling、Natural Language Processing Techniques

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.