A study of active learning methods for named entity recognition in clinical text
- 1 December 2015
- journal article
- research article
- Published by Elsevier in Journal of Biomedical Informatics
- Vol. 58, 11-18
Abstract
No abstract availableKeywords
Funding Information
- NIH (NLM 2R01LM010681-05)
This publication has 31 references indexed in Scilit:
- The Electronic Medical Records and Genomics (eMERGE) Network: past, present, and futureGenetics in Medicine, 2013
- Recognizing clinical entities in hospital discharge summaries using Structural Support Vector Machines with word representation featuresBMC Medical Informatics and Decision Making, 2013
- Applying active learning to assertion classification of concepts in clinical textJournal of Biomedical Informatics, 2012
- A study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summariesJournal of the American Medical Informatics Association, 2011
- Machine-learned solutions for three stages of clinical information extraction: the state of the art at i2b2 2010Journal of the American Medical Informatics Association, 2011
- 2010 i2b2/VA challenge on concepts, assertions, and relations in clinical textJournal of the American Medical Informatics Association, 2011
- Extracting medication information from clinical textJournal of the American Medical Informatics Association, 2010
- High accuracy information extraction of medication information from clinical notes: 2009 i2b2 medication extraction challengeJournal of the American Medical Informatics Association, 2010
- Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applicationsJournal of the American Medical Informatics Association, 2010
- What can natural language processing do for clinical decision support?Journal of Biomedical Informatics, 2009