Using nurses' natural language entries to build a concept-oriented terminology for patients' chief complaints in the emergency department

Journal of Biomedical Informatics

Volumn 36, Issue 4-5, 2003, Pages 260-270

  Travers, Debbie A ^a   Haas, Stephanie W ^a  

^a UNIVERSITY OF NORTH CAROLINA   (United States)

Author keywords

DEEDS; Emergency department; Natural language processing; Nursing; Terminology; UMLS

Indexed keywords

ARTICLE; AUTOMATION; CONTROLLED STUDY; DOCUMENTATION; EMERGENCY WARD; FIRST AID; HUMAN; LANGUAGE; MAJOR CLINICAL STUDY; METHODOLOGY; NOMENCLATURE; NURSE; NURSING; PATIENT COMPLIANCE; PRIORITY JOURNAL;

EID: 0345604429     PISSN: 15320464     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jbi.2003.09.007     Document Type: Article

References (72)

1. Emergency Nurses Association. Making the right decision: a triage curriculum (2nd ed.). Des Plaines, IL: Author 2001.
2. National Center for Injury Prevention and Control. Data elements for emergency department systems, release 1.0. Atlanta, FA: Centers for Disease Control and Prevention; 1997.
3. Lober W.B., Karras B.T., Wagner M.M.et al. Roundtable on bioterrorism detection: information system-based surveillance. J. Am. Med. Inform. Assoc. 9(2):2002;105-115.
4. Yasnoff W.A., Overhage J.M., Humphreys B.L., LaVenture M. A national agenda for public health informatics. J. Am. Med. Inform. Assoc. 8(6):2001;535-545.
5. Martinez R. Into the looking glass. Acad. Emerg. Med. 2:1995;83-84.
6. Garrison H.G., Runyan C.W., Tintinalli J.E.et al. Emergency department surveillance: an examination of issues and a proposal for a national strategy. Ann. Emerg. Med. 24:1994;849-856.
7. Wears R.L. Computer data base for ED visits. Ann. Emerg. Med. 21:1992;67-68.
8. Pollock D.A., Adams D.L., Bernardo L.M., Bradley V., Brandt M.D., Davis T.E. Data elements for emergency department systems (DEEDS), release 1.0: a summary report. J. Emerg. Nurs. 24:1998;35-44.
9. Bradley V. Innovative informatics: development of an emergency data set: a worthwhile challenge. J. Emerg. Nurs. 22(3):1996;238-240.
10. Bradley V. Toward a common language: emergency nursing uniform data set (ENUDS. J. Emerg. Nurs. 21(3):1995;248-250.
11. World Organization of National Colleges (WONCA), Academies and Academic Associations of General Practitioners/Family Physicians. International classification of primary care (ICPC-2) (2nd ed.) New York: Oxford University Press; 1998.
12. National Center for Health Statistics (NCHS). Reason for visit classification and coding manual. Hyattsville, Maryland: NCHS, Centers for Disease Control, US Department of Health and Human Services; 1997.
13. Campbell J.R., Carpenter P., Sneiderman C., Cohn S.P., Chute C.G., Warren J. Phase II evaluation of clinical coding schemes: completeness, terminology, mapping, definitions, and clarity. J. Am. Med. Inform. Assoc. 4:1997;238-250.
14. McDonald C.G. The barriers to electronic medical record systems and how to overcome them. J. Am. Med. Inform. Assoc. 4:1997;213-221.
15. Chute C.G., Cohn S.P., Campbell J.R. A framework for comprehensive health terminology systems in the United States: development guidelines, criteria for selection, and public policy implications. J. Am. Med. Inform. Assoc. 5:1998;503-510.
16. Werley H., Lang N. Identification of the nursing minimum data set. 1988;Springer Publishing Company, New York.
17. North American Nursing Diagnosis Association (NANDA). Nursing diagnoses: definitions and classifications 1999-2000. Philadelphia: Author 1999.
18. McCloskey J.C., Bulechck G.M. Iowa intervention project nursing interventions classification (NIC. third ed. 2000;Mosby-Year Book, St. Louis.
19. Bakken Henry S., Warren J.J., Lange L., Button P. A review of major nursing vocabularies and the extent to which they have the characteristics required for implementation in computer-based systems. J. Am. Med. Inform. Assoc. 5:1998;321-328.
20. Harris M.R., Graves J.R., Herrick L.M., Elkin P.L., Chute C.G. The content coverage and organizational structure of terminologies: the example of postoperative pain. Proc. AMIA Symp. 2000;335-339.
21. Bakken S., Warren J.J., Casey A., Konicek D., Lundberg C., Pooke M. Information model and terminology issues related to goals. Proc. AMIA Symp. 2002;17-21.
22. Ozbolt J. Terminology standards for nursing: collaboration at the summit. J. Am. Med. Inform. Assoc. 7:2000;517-522.
23. Beyea S.C. Data fields for intraoperative records using the Perioperative Nursing Data Set. AORN J. 73(5):2001;952-954.
24. Martin K.S., Scheet N.L. The Omaha system: applications for community health nursing. 1992;W.B. Saunders, Philadelphia.
25. Guidotti T.L. Bioterrorism and the public health response. Am. J. Prev. Med. 18:2000;178-180.
26. Cimino J.J. Desiderata for controlled medical vocabularies in the twenty-first century. Meth. Inform. Med. 37:1998;394-403.
27. National Library of Medicine (NLM), Unified Medical Language System (UMLS). Available from: http://www.nlm.nih.gov/research/umls/.
28. Liu H., Friedman C. A method for vocabulary development and visualization based on medical language processing and XML. Proc. AMIA Symp. 20000;502-506.
29. McCray A.T. The nature of lexical knowledge. Meth. Inform. Med. 37:1998;353-360.
30. Kreis C., Gorman P. Word frequency analysis of dictated clinical data: a user-centered approach to the design of a structure data entry interface. Proc. AMIA Symp. 1997;724-728.
31. Spyns P. Natural language processing in medicine: an overview. Meth. Inform. Med. 35:1996;285-301.
32. Johnson S.B., Gottfried M. Sublanguage analysis as a basis for a controlled medical vocabulary. Proc. Ann. Symp. Comput. App. Med. Care. 1989;519-523.
33. Friedman C., Kra P., Rzhetsky A. Two biomedical sublanguages: a description based on the theories of Zellig Harris. J. Biomed. Inform. 35(4):2002;222-235.
34. Harris Z. A theory of language and information: a mathematical approach. 1991;Clarendon Press, Oxford.
35. Grishman R., Kittredge R. Analyzing language in restricted domains: sublanguage description and processing. 1986;Lawrence Erlbaum, Hillsdale, NJ.
36. Haas S.W. Disciplinary variation in automatic sublanguage term identification. J. Am. Soc. Info. Sci. 28(1):1997;67-79.
37. Anderson J.D., Perez-Carballo J. The nature of indexing: how humans and machines analyze messages and texts for retrieval part 2: machine indexing and the allocation of human versus machine effort. Info. Proc. Man. 27(2):2001;255-277.
38. Aronson A.R., Bodenreider O., Chang H.F.et al. The NLM indexing initiative. Proc. AMIA Symp. 2000;17-21.
39. Ammons D. Psychology of the scientist: LXXIII. Miscommunication in technical writing. Percep Motor Skills. 86(2):1998;418.
40. Rindflesch T.C., Aronson A.R. Ambiguity resolution while mapping free text to the UMLS Metathesaurus. Proc. Annu. Symp. Comput. Appl. Med. Care. 1994;240-244.
41. Yu H., Hripcsak G., Friedman C. Mapping abbreviations to full forms in biomedical articles. J. Am. Med. Inform. Assoc. 9(3):2002;262-272.
42. Stetson P.D., Johnson S.B., Scotch M., Hripcsak G. The sublanguage of cross-coverage. Proc. AMIA Symp. 2002;742-746.
43. Dale R., Moisl H., Somers H. Handbook of natural language processing. 2000;Marcel Dekker, New York.
44. Haas S.W. Natural language processing: toward large-scale, robust systems. Williams M.E. Ann. Rev. Info. Sci. Tech. vol. 31:1996;83-119.
45. Sager J.C. A practical course in terminology processing. 1990;John Benjamins Publishing Company, Amsterdam.
46. National Library of Medicine (NLM), Unified Medical Language System (UMLS) 2003AA User Documentation. Available from: http://www.nlm.nih.gov/ research/umls/UMLSDOC.HTML .
47. Palmer D.D. Tokenisation and sentence segmentation. Dale R., Moisl H., Somers H. Handbook of natural language processing. 2000;11-35 Marcel Dekker, New York.
48. Travers D.A., Bodenreider O. Identifying medical concepts in free text chief complaint data. Acad. Emerg. Med. 9:2002;511. [abstract].
49. Porter M.F. An algorithm for suffix stripping. Program. 14:1980;130-137.
50. Yarowsky D. Word-sense disambiguation. Dale R., Moisl H., Somers H. Handbook of natural language processing. 2000;629-654 Marcel Dekker, New York.
51. Ide N., Veronis J. Introduction to the special issue on word sense disambiguation: the state of the art. Comp. Ling. 24(1):1998;1-40.
52. Olszewski R.T. Bayesian classification of triage diagnoses for the early detection of epidemics. Recent Adv. Artificial Intelligence: Proc 16th Int. FLAIRS Conf. 2003. 2003;412-416 AAAI Press, Menlo Park, California.
53. McCray A.T. The nature of lexical knowledge. Meth. Inform. Med. 37:1998;353-360.
54. Humphreys B.L., Lindberg D.A., Schoolman H.M., Barnett G.O. The unified medical language system: an informatics research collaboration. J. Am. Med. Inform. Assoc. 5:1998;1-11.
55. McCray A.T., Srinivasan S., Browne A.C. Lexical methods for managing variation in biomedical terminologies. Proc. Ann. Symp. Comput. App. Med. Care. 1994;235-239.
56. Payne T.H., Martin D.R. How useful is the UMLS Metathesaurus in developing a controlled vocabulary for an automated problem list? Proc. Ann. Symp. Comput. App. Med. Care. 1994;199-203.
57. Chute C.G., Elkin P.L. A clinically derived terminology: qualification to reduction. Proc. AMIA Symp. 1997;570-574.
58. Eisner J. The developing electronic curriculum consortium. J. Dent. Ed. 54:1990;598-599.
59. Cooper G.F., Miller R.A. An experiment comparing lexical and statistical methods for extracting MeSH terms from clinical free text. J. Am. Med. Inform. Assoc. 5:1998;62-75.
60. Christiansen T., Torkington N. Perl cookbook: solutions and examples for Perl programmers. 1998;O'Reilly, Sebastopol, CA.
61. Bodenreider O. Using UMLS semantics for classification purposes. Proc. AMIA Symp. 2000;86-90.
62. Schneiderman C.A., Rindflesch T.C., Bean C.A. Identification of anatomical terminology in medical text. Proc. AMIA Symp. 19980;428-432.
63. Huang X. Dealing with conjunctions in a machine translation environment. Proc. Coding. 1984;243-246.
64. Hirschman L. Conjunction in meta-restriction grammar. J. Logic. Prog. 3:1986;299-328.
65. Rindflesch T.R. Integrating natural language processing and biomedical domain knowledge for increased information retrieval effectiveness. Proc. 5th Ann. Dual-use Technol. App. Conf. 1995;260-265.
66. Dang H.T., Palmer M. Combining contextual features for word sense disambiguation. Proc. Word Sense Disambiguation: Recent Successes and Future Directions. 2002;88-94.
67. McCray A.T., Brown A.C. Discovering the modifiers in a terminology data set. Proc. AMIA Symp. 1998;780-784.
68. McCray A.T., Burgun A., Bodenreider O. Aggregating UMLS semantic types for reducing conceptual complexity. MEDINFO. 10:2001;216-220.
69. Aronsky D., Kendall D., Merkely K., James B.C., Haug P.J. A comprehensive set of coded chief complaints for the emergency department. Acad. Emerg. Med. 8(10):2001;980-989.
70. DiPasquale J.T., Nichols J.A., Garvey J.L. Chief complaint coding in the emergency department. Acad. Emerg. Med. 2(5):1995;442.
71. Afilalo M., Unger B., Boivin J.F.et al. Evaluation of a chief complaint classification system for comparing emergency department clienteles. Ann. Emerg. Med. 28(4):2001;S82. [abstract].
72. Haas SW, Travers DA, Waller AE, Hilligoss B, Cahill M, Pearce PF. Defining clinical similarity among ICD-9-CM diagnosis codes: diagnosis cluster schemes. In: Efthimiadis E, editor. Advances in Classification Research, vol. 12: Proceedings of the 12th ASIST SIG/CR Classification Research Workshop [in press].