Evaluation of a generalizable approach to clinical information retrieval using the automated retrieval console (ARC)

Journal of the American Medical Informatics Association

Volumn 17, Issue 4, 2010, Pages 375-382

  D'Avolio, Leonard W ^a,b   Nguyen, Thien M ^a   Farwell, Wildon R ^a,b,c   Chen, Yongming ^a   Fitzmeyer, Felicia ^a   Harris, Owen M ^a   Fiore, Louis D ^a,d,e  

^a VETERANS AFFAIRS MEDICAL CENTER   (United States)

^b BRIGHAM AND WOMEN S HOSPITAL   (United States)

^c VA BOSTON HEALTHCARE SYSTEM   (United States)

^d BOSTON UNIVERSITY SCHOOL OF PUBLIC HEALTH   (United States)

^e BOSTON UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY; ARTICLE; CLASSIFICATION ALGORITHM; CLASSIFIER; CLINICAL EVALUATION; COLORECTAL CANCER; COMPUTER PROGRAM; CONTROLLED STUDY; DEVICE RECALL; HUMAN; INFORMATION RETRIEVAL; INFORMATION SYSTEM; LUNG CANCER; MEDICAL INFORMATION; NATURAL LANGUAGE PROCESSING; PHYSICIAN; PROSTATE CANCER; REFERENCE DATABASE; ALGORITHM; CLASSIFICATION; COMPUTER INTERFACE; DATA MINING; INTERNATIONAL CLASSIFICATION OF DISEASES; MEDICAL RECORD; NEOPLASM; PATHOLOGY;

ALGORITHMS; DATA MINING; ELECTRONIC HEALTH RECORDS; HUMANS; INTERNATIONAL CLASSIFICATION OF DISEASES; NATURAL LANGUAGE PROCESSING; NEOPLASMS; SOFTWARE VALIDATION; USER-COMPUTER INTERFACE;

EID: 77958195980     PISSN: 10675027     EISSN: 1527974X     Source Type: Journal    
DOI: 10.1136/jamia.2009.001412     Document Type: Article

References (44)

1. U.S. Department of Health and Human Services. About healthy people, 2009. http://www.healthypeople.gov/About/.
2. Committee on Comparative Effectiveness Research Prioritization. Initial priorities for comparative effectiveness research. Washington DC: Institute of Medicine, 2009.
3. Bates D, Kuperman G, Wang S, et al. Ten commandments for effective clinical decision support: Making the practice of evidence-based medicine a reality. J Am Med Inform Assoc 2003;10:523-30.
4. Murphy S, Churchill S, Bry L, et al. Instrumenting the health care enterprise for discovery research in the genomic era. Genome Res 2009;19:1675-81.
5. Berg M, Goorman E. The contextual nature of medical information. Int J Med Inform 1999;56:51-60.
6. D'Avolio L. Electronic medical records at a crossroads: impetus for change or missed opportunity. J Am Med Assoc 2009;302:1109-10.
7. Burnum J. The misinformation era: the fall of the medical record. Ann Int Med 1989;110:482-4.
8. Musen M. The strained quality of medical data. Methods Inf Med 1989;28:123-5.
9. Peabody J, Luck J, Jain S, et al. Assessing the accuracy of administrative data in health information systems. Med Care 2005;42:1066-72.
10. Iezzoni L. Assessing quality using administrative data. Ann Int Med 1997;127:666-74.
11. Hersh W, Hickam D. Information retrieval in medicine: The SAPHIRE experience. J Am Soc Inf Sci 1995;46:743-7.
12. Friedman C, Alderson P, Austin J, et al. A general natural language text processor for clinical radiology. J Am Med Inform Assoc 1994;1:161-74.
13. Sager N, Friedman C, Lyman M, et al. Medical language processing: Computer management of narrative data. Reading, MA: Addison-Wesley, 1987.
14. Witten I, Frank E. Data mining: practical machine learning tools and techniques. 2nd edn. San Francisco: Morgan Kaufmann, 2005.
15. Marcus MP, Marcinkiewicz MA, Santorini B. Building a large annotated corpus of English: the penn treebank. Comput Linguist 1993;19:313-30.
16. Toutanova K, Klein D, Manning C, et al. Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network. Proceedings of HLT-NAACL 2003:252-9.
17. Ferrucci D, Lally A. UIMA: an architectural approach to unstructured information processing in the corporate research environment. NLE 2004;10:327-48.
18. Cunningham H. GATE, a general architecure for text engineering. Comput Hum 2004;36:223-54.
19. Zeng Q, Goryachev S, Weiss S, et al. Extracting principle diagnosis, co-morbidity, and smoking status for asthma research: evaluation of a natural language processing system. BMC Med Inform Decis Mak 2006;6:30.
20. Savova G, Kipper-Schuler K, Buntrok J, et al. UIMA-based clinical information extraction system. Language Resources and Evaluation Conference, Morocco, 2008.
21. Wellner B, Huyck M, Mardis S, et al. Rapidly retargetable approaches to deidentification. J Am Med Inform Assoc 2007;14:564-73.
22. Savova G, Clark C, Zheng J, et al. The Mayo/MITRE system for discovery of obesity and its comorbidities. Proceedings of the i2b2 Workshop in Challenges in Natural Language Processing for Clinical Data; 2009.
23. Harremoës P, Topsøe F. Maximum entropy fundamentals. Entropy 2001;3:191-226.
24. Koeling R. Chunking with maximum entropy models. Proceedings of CoNLL-2000 and LLL-2000, 2000:139-41.
25. Ratnaparkhi A. A maximum entropy part-of-speech tagger. Proceedings of the Empirical Methods in Natural Language Processing; 1996; University of Pennsylvania, May 1996:133-42.
26. Taira R, Soderland S. A statistical natural language processing for medical reports. Proceedings of the Annual Meeting of the American Medical Informatics Association, Washington DC, 1999:970-4.
27. Savova G, Kipper-Schuler K, Buntrock J, et al. UIMA-based clinical information extraction system. LREC 2008: Towards enhanced interoperability for large HLT systems: UIMA for NLP, Marrakech, Morocco, 2008.
28. Taira RK, Soderland SG. A statistical natural language processor for medical reports. Proc AMIA Symp 1999:970-4.
29. Lafferty J, McCallum A, Pereira F. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. International Conference on Machine Learning, San Francisco: Morgan Kaufman, 2001:282-9.
30. Peng F, McCallum A. Information extraction from research papers using conditional random fields. Inform Process Manag 2006;42:963-79.
31. National Institute of Standards and Technology. Message Understanding Conference Proceedings MUC-7 Table of Contents, 2001. http://www.itl.nist.gov/iaui/894.02/related_projects/muc/proceedings/muc_7_toc.html (accessed 08 June 2010).
32. National Institute of Standards and Technology. Introduction to information extraction, 2005. http://www-nlpir.nist.gov/related_projects/muc/ (accessed 31 Aug 2009).
33. Uzuner O, Goldstein I, Luo Y, et al. Identifying patient smoking status from medical discharge records. J Am Med Inform Assoc 2008;15:14-24.
34. Uzuner O. Recognizing obesity and comorbidities in sparse data. J Am Med Inform Assoc 2009;16:561-70.
35. Uzuner O, Luo Y, Szolovits P. Evaluating the state-of-the-art in automatic deidentification. J Am Med Inform Assoc 2007;14:550-63.
36. Friedman C, Shagina L, Lussier Y, et al. Automated encoding of clinical documents based on natural language processing. J Am Med Inform Assoc 2004;11:392-402.
37. Demner-Fushman D, Seckman C, Fisher C, et al. A Prototype System to Support Evidence-based Practice. Proceedings of the 2008 Annual Symposium of the American Medical Information Association; Washington, DC, 2008.
38. Dreyer K, Mannudeep K, Hurier A, et al. Application of a recently developed algorithm for automatic classification of unstructured radiology reports: validation study. Radiology 2005;234:323-9.
39. Denny JC, Smithers JD, Miller RA, et al. Understanding medical school curriculum content using KnowledgeMap. J Am Med Inform Assoc 2003;10:351-62.
40. Aronson A. Effective mapping of biomedical text to the UMLS metathesaurus: the MetaMap program. Proc AMIA Symp 2001:17-21.
41. McCallum A. MALLET: a machine learning for language toolkit, 2002. http://mallet.cs.umass.edu/.
42. IBM. OHNLP documentation and downloads. caBIG Knowledge center, 2009. https://cabig-kc.nci.nih.gov/Vocab/KC/index.php/OHNLP_Documentation_and_Downloads.
43. Salton G. The SMART retrieval system. Englewood Cliffs: Prentice Hall, 1971.
44. Salton G, Buckley C. Term-weighing approaches in automatic text retrieval. In: Spark Jones K, Willett P, eds. Readings in information retrieval. San Francisco: Morgan Kaufmann Publishers, Inc, 1987:323-7.