Comprehensive temporal information detection from clinical text: Medical events, time, and TLINK identification

Journal of the American Medical Informatics Association

Volumn 20, Issue 5, 2013, Pages 836-842

  Sohn Dr , Sunghwan ^a   Wagholikar, Kavishwar B ^a   Li, Dingcheng ^a   Jonnalagadda, Siddhartha R ^a   Tao, Cui ^a   Elayavilli, Ravikumar Komandur ^a   Liu, Hongfang ^a  

^a MAYO CLINIC   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APACHE; ARTICLE; AUTOMATION; DATA EXTRACTION; HOSPITAL DISCHARGE; HUMAN; INFORMATION PROCESSING; LANGUAGE; MACHINE LEARNING; MEASUREMENT ACCURACY; MEDICAL HISTORY; MEDICAL INFORMATION; TIME;

ARTIFICIAL INTELLIGENCE; ELECTRONIC HEALTH RECORDS; HUMANS; INFORMATION STORAGE AND RETRIEVAL; NATURAL LANGUAGE PROCESSING; PATIENT DISCHARGE SUMMARIES; TIME;

EID: 84881183249     PISSN: 10675027     EISSN: 1527974X     Source Type: Journal    
DOI: 10.1136/amiajnl-2013-001622     Document Type: Article

References (28)

1. Pustejovsky J, Castano J, Ingria R, et al. TimeML: Robust specification of event and temporal expressions in text. New Directions in Question Answering. In AAAI Spring Symposium on New Directions in Question-Answering, Stanford, CA, 2003:28-34.
2. Uzuner O, South BR, Shen S, et al. 2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text. J Am Med Inform Assoc 2011;18:552-6.
3. Torii M, Wagholikar K, Liu H. Using machine learning for concept extraction on clinical documents from multiple data sources. J Am Med Inform Assoc 2011;18:580-7.
4. Mani I, Wilson G. Annual Meeting on Association for Computational Linguistics, New Brunswick, NJ, 2000:69-76.
5. Verhagen M, Pustejovsky J. Temporal processing with the TARSQI toolkit. International Conference on Computational Linguistics, Manchester, UK. 2008:189-92.
6. Strötgen J, Gertz M. HeidelTime: High quality rule-based extraction and normalization of temporal expressions. International Workshop on Semantic Evaluation. Uppsala Sweden, 2010:321-4.
7. Verhagen M, Sauri R, Caselli T, et al. SemEval-2010 task 13: TempEval-2. International Workshop on Semantic Evaluation. Uppsala Sweden. 2010:57-62.
8. Chang AX, Manning CD. SUTIME: a library for recognizing and normalizing time expressions. International Conference on Language Resources and Evaluation (LREC 2012), Istanbul, Turkey, 2012:3735-40.
9. Savova G, Masanz J, Ogren P, 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.
10. Sohn S, Kocher J-PA, Chute CG, et al. Drug side effect extraction from clinical narratives of psychiatry and psychology patients. J Am Med Inform Assoc 2011;18 (Suppl 1):144-9.
11. Sohn S, Savova GK. Mayo Clinic Smoking Status Classification System: Extensions and Improvements. AMIA Annual Symposium; 2009, San Francisco, CA. 2009:619-23.
12. Kehler A. Resolving temporal relations using tense meaning and discourse interpretation. Formalizing the dynamics of information. Stanford: CSLI Publications, 2000.
13. Passonneau RJ. A computational model of the semantics of tense and aspect. Comput Linguist 1988;14:44-60.
14. Webber BL. Tense as discourse anaphor. Comput Linguist 1988;14:61-73.
15. Hwang CH, Schubert LK. Tense Trees as the fine structure of discourse. Annual Meeting on Association for Computational Linguistics, Newark, DE, 1992:232-40.
16. Lascarides A, Asher N. Temporal interpretation, discourse relations and commonsense entailment. Linguist Philos 1993;16:437-93.
17. Kamp H, Reyle U. From discourse to logic: introduction to modeltheoretic semantics of natural language, formal logic and discourse representation theory. The Netherlands: Kluwer Academic Dordrecht, 1993.
18. Mani I, Pustejovsky J. Temporal discourse models for narrative structure. ACL Workshop on Discourse Annotation, Barcelona, Spain, 2004:57-64.
19. Mani I, Verhagen M, Wellner B, et al. Machine learning of temporal relations. Annual Meeting on Association for Computational Linguistics. Sydney, Australia, 2006:753-60.
20. Wagholikar K, Torii M, Jonnalagadda S, et al. Feasibility of pooling annotated corpora for clinical concept extraction. AMIA Summits Transl Sci Proc 2012;2012:38.
21. Wagholikar K, Torii M, Jonnalagadda S, et al. Pooling annotated corpora for clinical concept extraction. J Biomed Semantics 2013. Published Online First.
22. 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.
23. McCallum, Andrew Kachites. MALLET: A Machine Learning for Language Toolkit. http://mallet.cs.umass.edu. 2002.
24. Liu HWS, Li D, Jonnalagadda S, et al. Towards a semantic lexicon for clinical natural language processing Annual Symposium of American Medical Informatics Association; Chicago: 2012.
25. Chapman WW, Bridewell W, Hanbury P, et al. A simple algorithm for identifying negated findings and diseases in discharge summaries. J Biomed Inform 2001;34:301-10.
26. Jonnalagadda SR, Li D, Sohn S, et al. Coreference analysis in clinical notes: a multi-pass sieve with alternate anaphora resolution modules. J Am Med Inform Assoc 2012;19:867-74.
27. Tao C, Solbrig HR, Chute CG. CNTRO 2.0: a harmonized semantic web ontology for temporal relation inferencing in clinical narratives. AMIA Summits Transl Sci Proc 2011:64-8.
28. Tao C, Wei WQ, Solbrig HR, et al. CNTRO: a semantic web ontology for temporal relation inferencing in clinical narratives. AMIA Annu Symp Proc 2010:787-91.