Combining rules and machine learning for extraction of temporal expressions and events from clinical narratives

Journal of the American Medical Informatics Association

Volumn 20, Issue 5, 2013, Pages 859-866

  Kovačević, Aleksandar ^a   Dehghan, Azad ^b   Filannino, Michele ^b   Keane, John A ^b   Nenadic, Goran ^b,c  

^a UNIVERSITY OF NOVI SAD   (Serbia)

^b UNIVERSITY OF MANCHESTER   (United Kingdom)

^c Kellogg Europe   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; EXTRACTION; MACHINE LEARNING; NARRATIVE; RECOGNITION; TASK PERFORMANCE; TEMPORAL CORTEX;

CLINICAL NLP; CLINICAL TEXT MINING; EVENT EXTRACTION; TERMPORAL EXPRESSION EXTRACTION; TERMPORAL EXPRESSION NORMALIZATION;

ARTIFICIAL INTELLIGENCE; ELECTRONIC HEALTH RECORDS; HUMANS; INFORMATION STORAGE AND RETRIEVAL; NATURAL LANGUAGE PROCESSING; TIME; TRANSLATIONAL MEDICAL RESEARCH;

EID: 84882747799     PISSN: 10675027     EISSN: 1527974X     Source Type: Journal    
DOI: 10.1136/amiajnl-2013-001625     Document Type: Article

References (50)

1. Wagholikar KB, Maclaughlin KL, Henry MR, et al. Clinical decision support with automated text processing for cervical cancer screening. J Am Med Inform Assoc 2012;19:833-83.
2. McGregor JI, Brooks CJ, Chalasani P, et al. The Health Informatics Trial Enhancement Project (HITE): Using routinely collected primary care data to identify potential participants for a depression trial. Trials 2010;11:39.
3. Ferrández O, South B, Shen S, et al. BoB, a best-8of-breed automated text de-identification system for VHA clinical documents. J Am Med Inform Assoc 2013;20:77-83.
4. Ogren PV, Savova GK, Buntrock JD, et al. Building and evaluating annotated corpora for medical NLP systems. AMIA Annual Symposium Proceedings; 2006:1050. Washington, DC, USA.
5. Sager N, Friedman C, Chi E, et al. The analysis and processing of clinical narrative. MedInfo 1986;2:1101-5.
6. Sager N, Friedman C, Lyman M. Medical language processing: computer management of narrative data. Reading, MA: Addison-Wesley, 1987.
7. Hripcsak G, Friedman C, Alderson PO, et al. Unlocking clinical data from narrative reports: a study of natural language processing. Ann Intern Med 1995;122:681-8.
8. 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.
9. Hripcsak G, Austin JH, Alderson PO, et al. Use of natural language processing to translate clinical information from a database of 889,921 chest radiographic reports. Radiology 2002;224:157-63.
10. Haug PJ, Koehler S, Lau LM, et al. Experience with a mixed semantic/syntactic parser. Proceedings of the Annual Symposium on Computer Application in Medical Care 1995:284-8. New Orleans, LA, USA.
11. Fiszman M, Chapman WW, Evans SR, et al. Automatic identification of pneumonia related concepts on chest x-ray reports. Proceedings of AMIA Symposium; 1999:67-71. Washington, DC, USA.
12. Haug PJ, Christensen L, Gundersen M, et al. A natural language parsing system for encoding admitting diagnoses. Proceedings AMIA Annual Fall Symposium; 1997:814-18. Nashville, TN, USA.
13. 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.
14. Zeng QT, Goryachev S, Weiss S, et al. Extracting principal diagnosis, co-morbidity and smoking status for asthma research: evaluation of a natural language processing system. BMC Med Inform Decis Mak 2006;6:30.
15. Spackman KA, Campbell KE, Cote RA. SNOMED-RT: a reference terminology for health care. Proceedings AMIA Annual Fall Symposium; 1997:640-4. Nashville, TN, USA.
16. World Health Organization. ICD-10 classifications of mental and behavioural disorder: clinical descriptions and diagnostic guidelines. Geneva: World Health Organization, 1992.
17. Lindberg DA, Humphreys BL, McCray AT. The unified medical language system. Methods Inf Med 1993;32:281-91.
18. Zeng Q, Cimino JJ. Automated knowledge extraction from the UMLS. Proceedings of AMIA Symposium; 1998:568-72. Orlando, FL, USA.
19. Bard J, Rhee SY, Ashburner M. An ontology for cell types. Genome Biol 2005;6:R21.
20. Smith CL, Goldsmith CA, Eppig JT. The mammalian phenotype ontology as a tool for annotating, analyzing, and comparing phenotypic information. Genome Biol 2005;6:R7.
21. Scheuermann RH, Ceusters W, Smith B. Toward an ontological treatment of disease and diagnosis. Summit on Translational Bioinformatics; 2009:116-20. San Francisco, CA, USA.
22. Aronson AR, Lang FM. An overview of MetaMap: historical perspective and recent advances. J Am Med Inform Assoc 2010;17:229-36.
23. Aronson AR. Effective mapping of biomedical text to the UMLS metathesaurus: the MetaMap program. Proceedings of AMIA Symposium; 2001:17-21. Washington, DC, USA.
24. Denny JC, Miller RA, Johnson KB, et al. Development and evaluation of a clinical note section header terminology. AMIA Annual Symposium Proceedings; 2008;156-60. Washington, DC, USA.
25. Uzuner Ö. Recognizing obesity and comorbidities in sparse data. J Am Med Inform Assoc 2009;16:561-70.
26. Solt I, Tikk D, Gál V, et al. Semantic classification of diseases in discharge summaries using a context-aware rulebased classifier. J Am Med Inform Assoc 2009;16:580-4.
27. Yang H, Spasic I, Keane J, et al. A text mining approach to the prediction of a disease status from clinical discharge summaries. J Am Med Inform Assoc 2009;16:596-600.
28. Uzuner Ö, Solti I, Cadag E. Extracting medication information from clinical text. J Am Med Inform Assoc 2010;17:514-18.
29. Spasic I, Sarafraz F, Keane JA, et al. Medication information extraction with linguistic pattern matching and semantic rules. J Am Med Inform Assoc 2010;17:532-5.
30. Uzuner Ö, 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.
31. Sun W, Rumshisky A, Uzuner Ö. Evaluating Temporal Relations in Clinical Text: 2012 i2b2 Challenge Overview. J Am Med Inform Assoc 2013;20:806-13.
32. de Bruijn B, Cherry C, Kiritchenko S, et al. Machine-learned solutions for three stages of clinical information extraction: the state of the art at i2b2 2010. J Am Med Inform Assoc 2011;18:557-62.
33. Gurulingappa H, Hofmann-Apitius M, Fluck J. Concept identification and assertion classification in patient health records. Proceedings of the 2010 i2b2/VA Workshop on Challenges in Natural Language Processing for Clinical Data; Boston, MA, USA, 2010.
34. Patrick JD, Nguyen DH, Wang Y, et al. A knowledge discovery and reuse pipeline for information extraction in clinical notes. J Am Med Inform Assoc 2011;18:574-9.
35. 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;18:601-6.
36. Kang N, Afzal Z, Singh B, et al. Using an ensemble system to improve concept extraction from clinical records. J Biomed Inform 2012;45:423-8.
37. Merrill GH. The MedDRA paradox. AMIA Annual Symposium Proceedings; 2008:470-4. Washington, DC, USA.
38. Wishart DS. DrugBank and its relevance to pharmacogenomics. Pharmacogenomics 2008;9:1155-62.
39. Verhagen M, Gaizauskas R, Schilder F, et al. SemEval-2007 task 15: TempEval temporal relation identification. Proceedings of the 4th International Workshop on Semantic Evaluations; Prague, Czech Republic: Association for Computational Linguistics, 2007:75-80.
40. Verhagen M, Saurí R, Caselli T, et al. SemEval-2010 task 13: TempEval-2. Proceedings of the 5th International Workshop on Semantic Evaluation; Los Angeles, CA: Association for Computational Linguistics, 2010:57-62.
41. Reeves RM, Ong FR, Matheny ME, et al. Detecting temporal expressions in medical narratives. Int J Med Inform 2013;82:118-2.
42. Verhagen M, Pustejovsky J. Temporal processing with the TARSQI toolkit. 22nd International Conference on Computational Linguistics: Demonstration Papers (COLING -08). Association for Computational Linguistics; Stroudsburg, PA, USA, 2008:189-92.
43. Lafferty JD, McCallum A, Pereira FCN. Conditional random fields: probabilistic models for segmenting and labeling sequence data. ICML; 2001: 282-9.
44. The Apache Software Foundation. http://opennlp.apache.org/ (accessed 3 Jan 2013).
45. Cunningham H, Maynard D, Bontcheva K, et al. GATE: a framework and graphical development environment for robust NLP tools and applications. Proceedings of the 40th Anniversary Meeting of the Association for Computational Linguistics (ACL'02); 2002. Philadelphia, PA, USA.
46. 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.
47. Choi JD, Palmer M. Transition-based semantic role labeling using predicate argument clustering. Proceedings of the ACL 2011 Workshop on Relational Models of Semantics. RELMS -11; Stroudsburg, PA, USA, 2011:37-45.
48. Llorens H, Saquete E, Navarro B. TIPSem (English and Spanish): evaluating CRFs and semantic roles in TempEval-2. Proceedings of the 5th International Workshop on Semantic Evaluation; Uppsala, Sweden, 2010:284-91.
49. Filannino M. Temporal expression normalisation in natural language texts, CoRR 2012; abs/1206.2010, 2012.
50. Goryachev S, Sordo M, Zeng QT, et al. Implementation and evaluation of four different methods of negation detection. Technical report, DSG, 2006.