A hybrid system for temporal information extraction from clinical text

Journal of the American Medical Informatics Association

Volumn 20, Issue 5, 2013, Pages 828-835

  Tang, Buzhou ^a,b   Wu, Yonghui ^a   Jiang, Min ^a   Chen, Yukun ^c   Denny, Joshua C ^c   Xu, Hua ^a,c  

^a UNIVERSITY OF TEXAS   (United States)

^b HARBIN INSTITUTE OF TECHNOLOGY   (China)

^c VANDERBILT UNIVERSITY MEDICAL CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; EXTRACTION; HUMAN; INFORMATION; NATURAL LANGUAGE PROCESSING; TASK PERFORMANCE; TEMPORAL CORTEX; TRAINING;

CLINIC EVENT EXTRACTION; MACHINE LEARNING; NATURAL LANGUAGE PROCESSING; TEMPORAL EXPRESSION EXTRACTION; TEMPORAL INFORMATION EXTRACTION; TEMPORAL RELATION EXTRACTION;

ARTIFICIAL INTELLIGENCE; ELECTRONIC HEALTH RECORDS; HUMANS; NATURAL LANGUAGE PROCESSING; PATIENT DISCHARGE SUMMARIES; TIME; TRANSLATIONAL MEDICAL RESEARCH;

EID: 84881139675     PISSN: 10675027     EISSN: 1527974X     Source Type: Journal    
DOI: 10.1136/amiajnl-2013-001635     Document Type: Article

References (30)

1. Pustejovsky J, Castaño J, Ingria R, et al. {TimeML:} Robust specification of event and temporal expressions in text. Fifth International Workshop on Computational Semantics {(IWCS-5)}, Tilburg, The Netherlands, 2003: 1-11.
2. Verhagen M, Gaizauskas R, Schilder F, et al. The TempEval challenge: identifying temporal relations in text. Lang Resources Eval 2009;43:161-79.
3. Verhagen M, Sauri R, Caselli T, et al. SemEval-2010 task 13: TempEval-2. Proceedings of the 5th International Workshop on Semantic Evaluation. Los Angeles, California: Association for Computational Linguistics, 2010: 57-62.
4. Sun W, Rumshisky A, Uzuner O. Evaluating Temporal Relations in Clinical Text: 2012 i2b2 Challenge Overview. J Am Med Inform Assoc 2013;20:806-13.
5. Allen JF. Maintaining knowledge about temporal intervals. Commun ACM 1983;26:832-43.
6. Sundheim BM. Overview of results of the MUC-6 evaluation. Proceedings of the 6th conference on Message Understanding. Columbia, Maryland: Association for Computational Linguistics, 1995: 13-31.
7. Chinchor N. Overview of MUC-7/MET-2. Proceedings of the 7th Message Understanding Conference, Fairfax, Virginia, 1998.
8. Mani I, Wilson G, Ferro L, et al. Guidelines for annotating temporal information. Proceedings of the First International Conference on Human Language Technology Research. San Diego: Association for Computational Linguistics, 2001:1-3.
9. Ferro L, Kozierok R, Gerber L, et al. Annotating temporal information: from theory to practice. Proceedings of the Second International Conference on Human Language Technology Research. San Diego, California: Morgan Kaufmann Publishers, 2002: 226-30.
10. Doddington G, Mitchell A, Przybocki M, et al. {The Automatic Content Extraction (ACE) Program-Tasks, Data, and Evaluation}. Proceedings of LREC 2004, Lisbon, Portugal, 2004: 837-40.
11. Setzer A. Temporal information in newswire articles: an annotation scheme and corpus study. University of Sheffield, UK, 2001.
12. Setzer A, Gaizauskas R. A pilot study on annotating temporal relations in text. Proceedings of the Workshop on Temporal and Spatial Information Processing-Volume 13. Association for Computational Linguistics, Toulouse, France, 2001: 1-8.
13. Pustejovsky J, Verhagen M. SemEval-2010 task 13: evaluating events, time expressions, and temporal relations (TempEval-2). Proceedings of the Workshop on Semantic Evaluations: Recent Achievements and Future Directions. Boulder, Colorado: Association for Computational Linguistics, 2009: 112-16.
14. Strötgen J, Gertz M. HeidelTime: high quality rule-based extraction and normalization of temporal expressions. Proceedings of the 5th International Workshop on Semantic Evaluation. Los Angeles, California: Association for Computational Linguistics, 2010: 321-4.
15. UzZaman N, Allen JF. TRIPS and TRIOS system for TempEval-2: extracting temporal information from text. Proceedings of the 5th International Workshop on Semantic Evaluation. Los Angeles, California: Association for Computational Linguistics, 2010: 276-83.
16. Zhou L, Friedman C, Parsons S, et al. System architecture for temporal information extraction, representation and reasoning in clinical narrative reports AMIA Annu Symp Proc 2005, Austin, TX, USA, 2005:869-873.
17. Savova G, Bethard S, Styler W, et al. Towards temporal relation discovery from the clinical narrative. AMIA Annual Symposium Proceedings/AMIA Symposium AMIA Symposium 2009, Washington DC, USA, 2009: 568-72.
18. Galescu L, Blaylock N. A corpus of clinical narratives annotated with temporal information. Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium. Miami, Florida, USA: ACM, 2012: 715-20.
19. Zhou L, Melton GB, Parsons S, et al. A temporal constraint structure for extracting temporal information from clinical narrative. J Biomed Inform 2006;39:424-39.
20. Gaizauskas R, Harkema H, Hepple M, et al. Task-oriented extraction of temporal information: the case of clinical narratives. Proceedings of the Thirteenth International Symposium on Temporal Representation and Reasoning. IEEE Computer Society, Washington DC, USA, 2006: 188-95.
21. Savova GK, Masanz JJ, Ogren PV, et al. Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications. J Am Med Inform Assoc 2010;17:507-13.
22. Reeves RM, Ong FR, Matheny ME, et al. Detecting temporal expressions in medical narratives. Int J Med Inform 2013;82:118-27.
23. Denny JC, Peterson JF, Choma NN, et al. Extracting timing and status descriptors for colonoscopy testing from electronic medical records. J Am Med Inform Assoc 2010;17:383-91.
24. Patnaik D, Butler P, Ramakrishnan N, et al. Experiences with mining temporal event sequences from electronic medical records: initial successes and some challenges. Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Diego, California, USA: ACM, 2011: 360-8.
25. Chapman WW, Chu D, Dowling JN. ConText: an algorithm for identifying contextual features from clinical text. Proceedings of the Workshop on BioNLP 2007: Biological, Translational, and Clinical Language Processing. Prague, Czech Republic: Association for Computational Linguistics, 2007: 81-8.
26. Friedman C, Alderson PO, Austin JH, et al. A general natural-language text processor for clinical radiology. J Am Med Inform Assoc 1994;1:161-74.
27. Zhou L, Hripcsak G. Temporal reasoning with medical data-a review with emphasis on medical natural language processing. J Biomed Inform 2007;40:183-202.
28. Jiang M, Chen Y, Liu M, et al. A study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summaries. J Am Med Inform Assoc 2011;18:601-30.
29. Tang B, Cao H, Wu Y, et al. Clinical entity recognition using structural support vector machines with rich features. ACM Sixth International Workshop on Data and Text Mining in Biomedical Informatics, Maui, HI, USA, 2012: 13-20.
30. Cer D, De Marneffe M-c, Jurafsky D, et al. Parsing to stanford dependencies: trade-offs between speed and accuracy. LREC, Floriana, Malta, 2010.