Cue-based assertion classification for Swedish clinical text—Developing a lexicon for pyConTextSwe

Artificial Intelligence in Medicine - Tập 61 - Trang 137-144 - 2014
Sumithra Velupillai1, Maria Skeppstedt1, Maria Kvist1,2, Danielle Mowery3, Brian E. Chapman4, Hercules Dalianis1, Wendy W. Chapman5
1Department of Computer and Systems Sciences (DSV), Stockholm University, Forum 100, 164 40 Kista, Sweden
2Department of Learning, Informatics, Management and Ethics (LIME), Karolinska Institutet, Widerström Building, Tomtebodavägen 18A, Solna, Sweden
3Department of Biomedical Informatics, University of Pittsburgh, 5607 Baum Boulevard, BAUM 423, Pittsburgh, PA 15206-3701, United States
4Department of Radiology, University of Utah, 729 Arapeen Drive, Salt Lake City, UT 84108, United States
5Department of Biomedical Informatics, University of Utah, 26 South 2000 East, Room 5775 HSEB, Salt Lake City, UT 84112-5775, United States

Tài liệu tham khảo

Horn, 1989 Uzuner, 2011, 2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text, JAMIA, 18, 552 Farkas, 2010, The CoNLL-2010 shared task: learning to detect hedges and their scope in natural language text, 1 Kim, 2009, Overview of BioNLP’09 shared task on event extraction, 1 Skeppstedt, 2011, Negation detection in Swedish clinical text: an adaption of NegEx to Swedish, Journal of Biomedical Semantics, 2, S3, 10.1186/2041-1480-2-S3-S3 Tanushi, 2013, Negation scope delimitation in clinical text using three approaches: NegEx, PyConTextNLP and SynNeg, 387 Deléger, 2012, Detecting negation of medical problem in French clinical notes, 697 Velupillai, 2011, Factuality Levels of Diagnoses in Swedish Clinical Text, 559 Velupillai, 2011, Automatic classification of factuality levels—a case study on Swedish diagnoses and the impact of local context Vincze, 2008, The BioScope corpus: biomedical texts annotated for uncertainty, negation and their scopes, BMC Bioinformatics, 9, S9, 10.1186/1471-2105-9-S11-S9 Chapman, 2011, Document-level classification of CT pulmonary angiography reports based on an extension of the ConText algorithm, J Biomed Inform, 44, 728, 10.1016/j.jbi.2011.03.011 Velupillai, 2013, Porting a rule-based assertion classifier for clinical Text from English to Swedish Friedman, 1994, A general natural language text processor for clinical radiology, JAMIA, 1, 161 deBruijn, 2011, Machine-learned solutions for three stages of clinical information extraction: the state of the art at i2b2 2010, JAMIA, 557 Chapman, 2001, A simple algorithm for identifying negated findings and diseases in discharge summaries, J Biomed Inform, 301, 10.1006/jbin.2001.1029 Mutalik, 2001, Use of general-purpose negation detection to augment concept indexing of medical documents a quantitative study using the umls, JAMIA, 8, 598 Aronow, 1999, Ad-hoc classification of radiology reports, JAMIA, 393 Uzuner, 2009, Machine learning and rule-based approaches to assertion classification, JAMIA, 16, 109 Clark, 2011, MITRE system for clinical assertion status classification, JAMIA, 18, 563 Goldin, 2003, Learning to detect negation with ‘Not’ in Medical Texts Morante, 2008, Learning the scope of negation in biomedical texts, 715 Agarwal, 2010, Biomedical negation scope detection with conditional random fields., JAMIA, 17, 696 Kilicoglu, 2008, Recognizing speculative language in biomedical research articles: a linguistically motivated perspective, BMC Bioinformatics, 9, S10, 10.1186/1471-2105-9-S11-S10 Harkema, 2009, Context: an algorithm for determining negation, experiencer, and temporal status from clinical reports, J Biomed Inform, 42, 839, 10.1016/j.jbi.2009.05.002 Wilson, 2011, Automated capture of pulmonary embolism spatial location in dictated reports using the ConText algorithm Gentili, 2011, Use of pyConText to classify reports containing critical results Gentili, 2012, Use of pyConText to Assist in Auditing for Chest Biopsy Complications Dalianis, 2012, Stockholm EPR Corpus: a clinical Database used to improve health care, 17 Velupillai, 2012 International Health Terminology Standards Development Organisation (IHTSDO), 2008 Knutsson, 2003, A robust shallow parser for Swedish