Document-level classification of CT pulmonary angiography reports based on an extension of the ConText algorithm

Journal of Biomedical Informatics

Volumn 44, Issue 5, 2011, Pages 728-737

  Chapman, Brian E ^a   Lee, Sean ^c   Kang, Hyunseok Peter ^b   Chapman, Wendy W ^a  

^a UNIVERSITY OF CALIFORNIA SAN DIEGO   (United States)

^b STANFORD UNIVERSITY SCHOOL OF MEDICINE   (United States)

^c UNIVERSITY OF PITTSBURGH SCHOOL OF MEDICINE   (United States)

Author keywords

Computed tomography; ConText; CTPA; Medical language processing; Pulmonary emboli

Indexed keywords

COMPUTED TOMOGRAPHY; CONTEXT; CTPA; MEDICAL LANGUAGE PROCESSING; PULMONARY EMBOLUS;

ALGORITHMS; ANGIOGRAPHY; CLASSIFIERS; COMPUTERIZED TOMOGRAPHY; DIAGNOSIS; TEXT PROCESSING; TOMOGRAPHY;

INFORMATION RETRIEVAL SYSTEMS;

ALGORITHM; ARTICLE; CLASSIFICATION; COMPUTED TOMOGRAPHIC ANGIOGRAPHY; CONSENSUS; DIAGNOSTIC VALUE; DISEASE CLASSIFICATION; DISEASE SEVERITY; DOCUMENT LEVEL CLASSIFICATION; GOLD STANDARD; HUMAN; LUNG ANGIOGRAPHY; LUNG EMBOLISM; MEDICAL INFORMATION; PREDICTIVE VALUE; PRIORITY JOURNAL; QUALITATIVE ANALYSIS; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; ANGIOGRAPHY; BAYES THEOREM; HUMANS; LUNG; PULMONARY EMBOLISM; RESEARCH REPORT; SEMANTICS;

EID: 80052875180     PISSN: 15320464     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jbi.2011.03.011     Document Type: Article

References (44)

1. Abujudeh H.H., Kaewlai R., Farsad K., Orr E., Gilman M., Shepard J.A. Computed tomography pulmonary angiography: an assessment of the radiology report. Acad Radiol 2009, 16:1309-1315.
2. Aronis JM, Cooper GF, Kayaalp M, Buchanan BG. Identifying patient subgroups with simple Bayes'. Proc AMIA Symp; 1999: 658-62.
3. Aronson AR. Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program. Proc AMIA Symp 2001:17-21.
4. Berlin L. Pitfalls of the vague radiology report. AJR Am J Roentgenol 2000, 174:1511-1518.
5. Chapman W.W., Bridewell W., Hanbury P., Cooper G.F., Buchanan B.G. A simple algorithm for identifying negated findings and diseases in discharge summaries. J Biomed Inform 2001, 34:301-310.
6. Chapman W.W., Christensen L.M., Wagner M.M., Haug P.J., Ivanov O., Dowling J.N., et al. Classifying free-text triage chief complaints into syndromic categories with natural language processing. Artif Intell Med 2005, 33:31-40.
7. Chapman W.W., Cooper G.F., Hanbury P., Chapman B.E., Harrison L.H., Wagner M.M. Creating a text classifier to detect radiology reports describing mediastinal findings associated with inhalational anthrax and other disorders. J Am Med Inform Assoc 2003, 10:494-503.
8. Chapman W.W., Fiszman M., Frederick P.R., Chapman B.E., Haug P.J. Quantifying the characteristics of unambiguous chest radiography reports in the context of pneumonia. Acad Radiol 2001, 8:57-66.
9. Christensen LM, Harkema H, Haug PJ, Irwin JY, Chapman WW. Onyx: a system for the semantic analysis of clinical text. In: BioNLP '09: proceedings of the workshop on BioNLP. Association for Computational Linguistics, Morristown, NJ, USA; 2009. p. 19-27.
10. Christensen LM, Haug PJ, Fiszman M. Mplus: a probabilistic medical language understanding system. In: Proceedings of the ACL-02 workshop on natural language processing in the biomedical domain. Association for Computational Linguistics, Morristown, NJ, USA; 2002. p. 29-36.
11. Cohen J. Weighted kappa: nominal scale agreement with provision for scaled disagreement or partial credit. Psychol Bull 1968, 70:213-220.
12. Daume H. Beating an n-gram; 2006. http://nlpers.blogspot.com/2006/06/beating-n-gram.html.
13. Denny JC, Peterson JF, Choma NN, Xu H, Miller RA, Bastarache L, et al. Development of a natural language processing system to identify timing and status of colonoscopy testing in electronic medical records. AMIA Annu Symp Proc 2009 2009:141.
14. Dublin S., Glazer N.L., Smith N.L., Psaty B.M., Lumley T., Wiggins K.L., et al. Diabetes mellitus, glycemic control, and risk of atrial fibrillation. J Gen Intern Med 2010.
15. Elkin P.L., Brown S.H., Bauer B.A., Husser C.S., Carruth W., Bergstrom L.R., et al. A controlled trial of automated classification of negation from clinical notes. BMC Med Inform Decis Mak 2005, 5:13.
16. Fedullo P.F., Tapson V.F. Clinical practice. The evaluation of suspected pulmonary embolism. N Engl J Med 2003, 349:1247-1256.
17. Fisher E.S., Whaley F.S., Krushat W.M., Malenka D.J., Fleming C., Baron J.A., et al. The accuracy of Medicare's hospital claims data: progress has been made, but problems remain. Am J Public Health 1992, 82:243-248.
18. Fiszman M., Chapman W.W., Aronsky D., Evans R.S., Haug P.J. Automatic detection of acute bacterial pneumonia from chest X-ray reports. J Am Med Inform Assoc 2000, 7:593-604.
19. Friedman C. A broad-coverage natural language processing system. Proc AMIA Symp 2000:270-4.
20. Gupta D., Saul M., Gilbertson J. Evaluation of a deidentification (De-Id) software engine to share pathology reports and clinical documents for research. Am J Clin Pathol 2004, 121:176-186.
21. Haque R., Chiu V., Mehta K.R., Geiger A.M. An automated data algorithm to distinguish screening and diagnostic colorectal cancer endoscopy exams. J Natl Cancer Inst Monogr 2005, 116-118.
22. Harkema H., Dowling J.N., Thornblade T., Chapman W.W. ConText: an algorithm for determining negation, experiencer, and temporal status from clinical reports. J Biomed Inform 2009, 42:839-851.
23. Hatabu H., Hunsaker A.R. The cost and consequence of uncertainty. Acad Radiol 2009, 16:1307-1308.
24. Huang Y, Lowe H. A grammar-based classification of negations in clinical radiology reports. Proc AMIA Annu Fall Symp 2005:988.
25. Jha A.K., Kuperman G.J., Teich J.M., Leape L., Shea B., Rittenberg E., et al. Identifying adverse drug events: development of a computer-based monitor and comparison with chart review and stimulated voluntary report. J Am Med Inform Assoc 1998, 5:305-314.
26. Jones K.S. A statistical interpretation of term specificity and its application in retrieval. J Doc 1972, 28:11-21.
27. Kaushal R., Bates D.W., Franz C., Soukup J.R., Rothschild J.M. Costs of adverse events in intensive care units. Crit Care Med 2007, 35:2479-2483.
28. Mowery DL, Harkema H, Dowling J, Lustgarten J, Chapman WW. Distinguishing historical from current problems in clinical reports - which textual features help? In: BioNLP workshop of the 47th annual meeting of the Association of Computational Linguistics, Boulder, CO; 2009.
29. Mowery DL, Harkema H, Dowling JN, Lustgarten JL, Chapman WW. Distinguishing historical from current problems in clinical reports: which textual features help? In: BioNLP '09: proceedings of the workshop on BioNLP. Association for Computational Linguistics, Morristown, NJ, USA; 2009. p. 10-8.
30. Mutalik P.G., Deshpande A., Nadkarni P.M. Use of general-purpose negation detection to augment concept indexing of medical documents: a quantitative study using the UMLS. J Am Med Inform Assoc 2001, 8:598-609.
31. Nelson J.C., Jackson M., Yu O., Whitney C.G., Bounds L., Bittner R., et al. Impact of the introduction of pneumococcal conjugate vaccine on rates of community acquired pneumonia in children and adults. Vaccine 2008, 26:4947-4954.
32. Pakhomov S., Bjornsen S., Hanson P., Smith S. Quality performance measurement using the text of electronic medical records. Med Decis Making 2008, 28:462-470.
33. Savova G, Bethard S, Styler W, Martin J, Palmer M, Masanz J, et al. Towards temporal relation discovery from the clinical narrative. AMIA Annu Symp Proc 2009 2009:568-2.
34. Savova G.K., Masanz J.J., Ogren P.V., Zheng J., Sohn S., Kipper-Schuler K.C., et al. Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications. J Am Med Inform Assoc 2010, 17(5):507-513.
35. South B.R., Phansalkar S., Swaminathan A.D., Anthony J., Delisle S., Perl T., et al. Text-processing of VA clinical notes to improve case detection models for influenza-like illness. Adv Dis Surveill 2007, 2:28.
36. Trick W.E., Chapman W.W., Wisniewski M.F., Peterson B.J., Solomon S.L., Weinstein R.A., et al. Electronic interpretation of chest radiograph reports to detect central venous catheters. Infect Control Hosp Epidemiol 2003, 24:950-954.
37. Uzuner Ö., Zhang X., Sibanda T. Machine learning and rule-based approaches to assertion classification. J Am Med Inform Assoc 2009, 16(1):109-115.
38. Uzuner O., Zhang X., Sibanda T. Machine learning and rule-based approaches to assertion classification. J Am Med Inform Assoc 2009, 16:109-115.
39. Wahner-Roedler DL, Welsh GA, Trusko BE, Froehling DA, Froehling DA, Temesgen Z, et al. Using natural language processing for identification of pneumonia cases from clinical records of patients with serologically proven influenza. AMIA Annu Symp Proc 2008; 1165.
40. Wilson RA. Automated ancillary cancer history classification for mesothelioma patients from free-text clinical reports. Master's thesis, University of Pittsburgh; 2010.
41. Wilson R.A., Chapman W.W., DeFries S.J., Becich M.J., Chapman B.E. Automated ancillary cancer history classification for mesothelioma patients from free-text clinical reports. J Pathol Inform TBD 2010, 1:24.
42. Yount R., Vries J., CD C. The medical archival system: an information retrieval system based on distributed parallel processing. Inf Process Manage 1991, 27:379-391.
43. Zeng Q.T., Goryachev S., Weiss S., Sordo M., Murphy S.N., Lazarus R. 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.
44. Zhou L., Melton G.B., Parsons S., Hripcsak G. A temporal constraint structure for extracting temporal information from clinical narrative. J Biomed Inform 2006, 39(4):424-439.