Task 2: ShARe/CLEF eHealth evaluation lab 2013

CEUR Workshop Proceedings

Volumn 1179, Issue , 2013, Pages

  Mowery, Danielle L ^a   South, Brett R ^b   Christensen, Lee ^b   Murtola, Laura Maria ^c   Salanterä, Sanna ^c   Suominen, Hanna ^d   Martinez, David ^e   Elhadad, Noemie ^f   Pradhan, Sameer ^g   Savova, Guergana ^g   Chapman, Wendy W ^h  

^a UNIVERSITY OF PITTSBURGH   (United States)

^b UNIVERSITY OF UTAH   (United States)

^c UNIVERSITY OF TURKU   (Finland)

^d AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

^e UNIVERSITY OF MELBOURNE   (Australia)

^f Columbia University ^*  (United States)

^g HARVARD UNIVERSITY   (United States)

^h UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Natural language processing; Reference standard generation; Text normalization

Indexed keywords

ACRONYMS AND ABBREVIATIONS; EHEALTH; NATURAL LANGUAGE PROCESSING; PILOT STUDIES; REFERENCE STANDARD; TEXT NORMALIZATIONS;

NATURAL LANGUAGE PROCESSING SYSTEMS;

EID: 84922032587     PISSN: 16130073     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (38)

1. Medical Protection Society. Online medical records and the doctor-patient partnership. MPS research report (2013)
2. Ross, S., Lin, C. The eé cts of promoting patient access to medical records. A review. J Am Med Inform Assoc 10(2) (2003) 129-138
3. Delbanco, T., Walker, J., Bell, S., Darer, J., Elmore, J., Farag, N., Feldman, H., Mejilla, R., Ngo, L., Ralston, J., Ross, S., Trivedi, N., Vodicaka, E., Leveille, S. Inviting patients to dread their doctors' notes. A quasi-experimental study and look ahead. Ann Intern Med 157(7) (2012) 461-470
4. Engel, K., Buckley, B., Forth, V., McCarthy, D., Ellison, E., Schmidt, M., Adams, J. Patient understanding of emergency department discharge summary instructions. Where are knowledge deficits greatest? Acad Emerg Med 19(9) (2012) E1035-E1044
5. Meystre, S., Savova, G., Kipper-Schuler, K., Hurdle, J. Extracting information from textual documents in the electronic health record. A review of recent research. Yearb Med Inform 35 (2008) 128-44
6. Pestian, J., Brew, C., Matykiewicz, P., Hovermale, D., Johnson, N., Cohen, K., Duch, W. A shared task involving multi-label classification of clinical free text. BioNLP 2007. Biological, translational, and clinical language processing (2007) 97-104
7. Uzuner, O., Mailoa, J., Ryan, R., Sibanda, T. Semantic relations for problemoriented medical records. Artif Intell Med 50(2) (October 2010) 63-73
8. Uzuner, O., Luo, Y., Szolovits, P. Viewpoint paper. Evaluating the state-of-The-Art in automatic de-identification. J Am Med Inform Assoc 14(5) (2007) 550-563
9. Uzuner, O., Goldstein, I., Luo, Y., Kohane, I. Identifying patient smoking status from medical discharge records. J Am Med Inform Assoc 15 (2008) 14-24.
10. Uzuner, O. Recognizing obesity and co-morbidities in sparse data. J Am Med Inform Assoc 16(4) (2009) 561-570
11. Uzuner, O., Solti, I., Cadag, E. Extracting medication information from clinical text. J Am Med Inform Assoc 17(5) (2010) 514-518
12. Grishman, R., Sundheim, B. Message Understanding Conference-6. A brief history. In. Proceedings of the 16th conference on Computational linguistics-Volume 1 COLING '96, Stroudsburg, PA, USA, Association for Computational Linguistics (1996) 466-471
13. Hersh, W., Bhupatiraju, R., Corley, S. Enhancing access to the Bibliome. The TREC Genomics Track. Stud Health Technol Inform 107(Pt 2) (2004) 773-7
14. Jones, K. Reections on TREC. In. Information Processing &Management 31(3). (1995) 29131-4
15. Roberts, A., Gaizauskas, R., Hepple, M., Davis, N., Demetriou, G., Guo, Y., Kola, J., Roberts, I., Setzer, A., Tapuria, A., Wheeldin, B. The CLEF Corpus. Semantic Annotation of Clinical Text. In. AMIA Annu Symp Proc. (2007) 625-629
16. Roberts, A., Gaizauskas, R., Hepple, M., Davis, N., Demetriou, G., Guo, Y., Roberts, I., Setzer, A. Building a semantically annotated corpus of clinical texts. J Biomed Inform 42(5) (2009) 950-66
17. Kim, J., Ohta, T., Tateisi, Y., Tsujii, J. GENIA corpus-A semantically annotated corpus for bio-textmining. In. ISMB (Supplement of Bioinformatics). (2003) 180-182
18. Kim, J., Ohta, T., Tsujii, J. Corpus annotation for mining biomedical events from literature. BMC Bioinformatics 9 (2008)
19. Marcus, M., Beatrice, S., Marcinkiewicz, M. Building a large annotated corpus of english. The Penn Treebank. Computational Linguistics 19(2) (1993) 313-330
20. Savova, G., Coden, A., Sominsky, I., Johnson, R., Ogren, P., Groen, P.d., Chute, C. Word sense disambiguation across two domains. Biomedical literature and clinical notes. J Biomed Inform 41(6) (December 2008) 1088-1100
21. Savova, G., Ogren, P., Duý, P., Buntrock, J., Chute, C. Technical brief. Mayo Clinic NLP system for patient smoking status identification. J Am Med Inform Assoc 15 (2008) 25-28
22. Chapman, W., Dowling, J., Hripcsak, G. Evaluation of training with an annotation schema for manual annotation of clinical conditions from emergency department reports. Int J Med Inform 77(2) (2008) 107-13
23. Chapman, W., Bridewell, W., Hanbury, P., Cooper, G., Buchanan, B. A simple algorithm for identifying negated findings and diseases in discharge summaries. J Biomed Inform 2001 (2001) 34-301
24. Chapman, W., Haug, P. Comparing expert systems for identifying chest x-ray reports that support pneumonia. In. AMIA Annu Symp Proc. (1999) 216-220
25. Uzuner, O., Solti, I., Xia, F., Cadag, E. Community annotation experiment for ground truth generation for the i2B2 medication challenge. J Am Med Inform Assoc 17(5) (2010) 519-523
26. Uzuner, O., South, B., Shen, S., DuVall, S. 2010 i2B2/VA challenge on concepts, assertions, and relations in clinical text. J Am Med Inform Assoc 18(5) (2011) 552-556
27. Xu, H., Stetson, P., Friedman, C. A study of abbreviations in clinical notes. In. AMIA Annu Symp Proc. (2007) 821-825
28. Campbell, K., Oliver, D., Shortlié, E. The Unified Medical Language System. Towards a collaborative approach for solving terminologic problems. J Am Med Inform Assoc 5(1) (1998) 12-16
29. Wu, Y., Denny, J.C., Rosenbloom, S.T., Miller, R.A., Giuse, D.A., Xu, H. A comparative study of current clinical natural language processing systems on handling abbreviations in discharge summaries. AMIA Annu Symp Proc 2012 (2012) 997-1003
30. Moon, S., Pakhomov, S., Melton, G. Automated disambiguation of acronyms and abbreviations in clinical texts. Window and training size considerations. In. AMIA Annu Symp Proc. (2012) 1310-1319
31. CHV. Consumer Health Vocabulary. http: //consumerhealthvocab.org/ Accessed. 2013-06-30.
32. Saeed, M., Lieu, C., Raber, G., Mark, R. MIMIC II. A massive temporal ICU patient database to support research in intelligent patient monitoring. Comput Cardiol 29 (2002)
33. NLTK. Natural Language ToolKit. http: //nltk.org/ Accessed. 2013-06-30.
34. Fana, F. NLP Ecosystem. Annotation Registry. http: //nlpecosystem. ucsd.edu/annotators Accessed. 2013-06-30.
35. CITI. Collaborative Institutional Training Initiative. https: //www.citiprogram.org/ Accessed. 2013-06-30.
36. NIH. National Institute of Health-ethics training module. http: //ethics.od.nih.gov/Training/AET.htm Accessed. 2013-06-30.
37. Physionet. Physionet site. https: http: //www.physionet.org/ Accessed. 2013-06-30.
38. ShARe. ShARe CLEF eHealthWebsite. https: //sites.google.com/site/shareclefehealth/data#TOCObtaining-Datasets-Tasks-1-And-2-/ Accessed. 2013-06-30