Using local lexicalized rules to identify heart disease risk factors in clinical notes

Journal of Biomedical Informatics - Tập 58 - Trang S183-S188 - 2015
George Karystianis1,2, Azad Dehghan1,2, Aleksandar Kovacevic3, John A. Keane1,4, Goran Nenadic1,5,4
1School of Computer Science, University of Manchester, Manchester, UK
2The Christie NHS Foundation Trust, Manchester, UK
3Faculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia
4Manchester Institute of Biotechnology, University of Manchester, UK
5Health eResearch Centre, The Farr Institute of Health Informatics Research, Manchester, UK

Tài liệu tham khảo

World Health Organization. The Top 10 Causes of Death. <http://www.who.int/mediacentre/factsheets/fs310/en/>. Shah, 2015, High sensitivity cardiac troponin and the under-diagnosis of myocardial infarction in women: prospective cohort study, BMJ, 350, g7873, 10.1136/bmj.g7873 A. Stubbs, C. Kotfila, H. Xu, Ö. Uzuner, Practical Applications for NLP in Clinical Research: the 2014 i2b2/UTHealth Shared Tasks, J. Biomed. Inform 58S (2015) S1–S5. Friedman, 2004, Automated encoding of clinical documents based on natural language processing, J. Am. Med. Inform. Assoc., 11, 392, 10.1197/jamia.M1552 Savova, 2010, Mayo clinical text analysis and knowledge extraction system (cTAKES): architecture, component evaluation and applications, J. Am. Med. Inform. Assoc., 17, 507, 10.1136/jamia.2009.001560 Spasić, 2014, Text mining of cancer-related information: review of current status and future directions, Int. J. Med. Inform., 83, 605, 10.1016/j.ijmedinf.2014.06.009 Sohn, 2011, Drug side effect extraction from clinical narratives of psychiatry and psychology patients, J. Am. Med. Inform. Assoc., 18, i144, 10.1136/amiajnl-2011-000351 S. Goryachev, K. Hyeoneui, Z.T. Qing, Identification and extraction of family history information from clinical reports, in: AMIA Annual Symposium Proceedings, vol. 2008, American Medical Informatics Association, 2008. Y. Wang, Annotating and recognising named entities in clinical notes, in: Proceedings of the ACL-IJCNLP 2009 Student Research Workshop, Association for Computational Linguistics, 2009. Patrick, 2009, High accuracy information extraction of medication information from clinical notes: 2009 i2b2 medication extraction challenge, J. Am. Med. Inform. Assoc., 17, 524 Spasic, 2010, Medication information extraction with linguistic pattern matching and semantic rules, J. Am. Med. Inform. Assoc., 17, 532, 10.1136/jamia.2010.003657 Yang, 2010, Automatic extraction of medication information from medical discharge summaries, J. Am. Med. Inform. Assoc., 17, 545, 10.1136/jamia.2010.003863 Rink, 2011, Automatic extraction of relations between medical concepts in clinical texts, J. Am. Med. Inform. Assoc., 18, 594, 10.1136/amiajnl-2011-000153 Jonnalagadda, 2012, Enhancing clinical concept extraction with distributional semantics, J. Biomed. Inform., 45, 129, 10.1016/j.jbi.2011.10.007 Xu, 2012, Feature engineering combined with machine learning and rule-based methods for structured information extraction from narrative clinical discharge summaries, J. Am. Med. Inform. Assoc., 19, 824, 10.1136/amiajnl-2011-000776 M. Fiszman, G. Rosemblat, C.B. Ahlers, T.C. Rindflesch, Identifying risk factors for metabolic syndrome in biomedical text, in: AMIA Annual Symposium Proceedings, Vol. 2007, American Medical Informatics Association, 2007. Uzuner, 2010, Extracting medication information from clinical text, J. Am. Med. Inform. Assoc., 17, 514, 10.1136/jamia.2010.003947 Doan, 2012, Recognition of medication information from discharge summaries using ensembles of classifiers, BMC Med. Inform. Decis. Mak., 12, 36, 10.1186/1472-6947-12-36 Deleger, 2010, Extracting medication information from narrative patient records: the case of medication-related information, J. Am. Med. Inform. Assoc., 17, 555, 10.1136/jamia.2010.003962 Uzuner, 2009, Recognizing obesity and comorbidities in sparse data, J. Am. Med. Inform. Assoc., 16, 561, 10.1197/jamia.M3115 UMLS, 2014. <http://www.nlm.nih.gov/research/umls/>. W.W. Cohen, MinorThird: Methods for Identifying Names and Ontological Relations in Text Using Heuristics for Inducing Regularities from Data, 2004. <http://github.com/TeamCohen/MinorThird/>. A. Kovacevic, A. Dehghan, M. Filannino, J. Keane, G. Nenadic, Combining rules and machine learning for extraction of temporal expressions and events from clinical narratives, J. Am. Med. Inform. Assoc. http://dx.doi.org/10.1136/amiajnl-2013-00.