Named entity recognition of follow-up and time information in 20 000 radiology reports

Journal of the American Medical Informatics Association

Volumn 19, Issue 5, 2012, Pages 792-799

  Xu, Yan ^a,b   Tsujii, Junichi ^b   Chang, Eric I Chao ^b  

^a BEIHANG UNIVERSITY   (China)

^b MICROSOFT RESEARCH ASIA   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; EFFECT SIZE; FOLLOW UP; HUMAN; MACHINE LEARNING; MEDICAL INFORMATION; MEDICAL RECORD; RADIOLOGY; ARTIFICIAL INTELLIGENCE; CLASSIFICATION; DATA MINING; ELECTRONIC MEDICAL RECORD; FEASIBILITY STUDY; HOSPITAL INFORMATION SYSTEM; METHODOLOGY; NATURAL LANGUAGE PROCESSING;

ARTIFICIAL INTELLIGENCE; DATA MINING; ELECTRONIC HEALTH RECORDS; FEASIBILITY STUDIES; HUMANS; NATURAL LANGUAGE PROCESSING; RADIOLOGY INFORMATION SYSTEMS;

EID: 84872259831     PISSN: 10675027     EISSN: 1527974X     Source Type: Journal    
DOI: 10.1136/amiajnl-2012-000812     Document Type: Article

References (41)

1. Spyns P. Natural language processing in medicine: an overview. Methods Inf Med 1996;35:285-301.
2. 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.
3. Meystre SM, Haug PJ. Comparing natural language processing tools to extract medical problems from narrative text. AMIA Annu Symp Proc 2005:525-9.
4. Meystre SM, Haug PJ. Natural language processing to extract medical problems from electronic clinical documents: performance evaluation. J Biomed Inform 2006;39:589-99.
5. Sager N Lyman M, Bucknall C, et al. Natural language processing and the representation of clinical data. J Am Med Inform Assoc 1994;2:142-60.
6. 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.
7. 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.
8. Huang Y, Lowe HJ, Klein D, et al. Improved identification of noun phrases in clinical radiology reports using a high-performance statistical natural language parser augmented with the UMLS specialist lexicon. J Am Med Inform Assoc 2005;12:275-85.
9. Bashyam V, Taira RK. A study of lexical behavior of sentences in chest radiology reports. AMIA Annu Symp Proc 2005:891.
10. Friedlin J, McDonald CJ. A natural language processing system to extract and code concepts relating to congestive heart failure from chest radiology reports. AMIA Annu Symp Proc 2006:269-73.
11. Elkin PL, Froehling D, Wahner-Roedler D, et al. NLP-based Identification of Pneumonia Cases from Free-text Radiological Reports. AMIA Annu Symp Proc 2005:172-6.
12. Denny JC, Peterson JF, Choma NN, et al. Extracting timing and status descriptors for colonoscopy testing from electronic medical records. J Am Med Inform Assoc 2010;17:383-8.
13. Lafferty J, McCallum A, Pereira F. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. Proceedings of the 18th International Conference on Machine Learning (ICML 2001). Williamstown, MA, USA: International Machine Learning Society, 2001:282-9.
14. Li X, Wang YY, Acero A. Extracting structured information from user queries with semi-supervised conditional random fields. Proceedings of the 32nd ACM Special Interest Group on Information Retrieval (SIGIR 2009). Boston, MA, USA ACM Special Interest Group on Information Retrieval, 2009:572-9.
15. Agrawal R, Srikant R. Mining sequential patterns. Proceedings of the 11th International Conference on Data Engineering (ICDE 1995). Taipei, Taiwan: International Conference on Data Engineering, 1995:3-14.
16. Ren JD, Zhou XL. A new incremental updating algorithm for mining sequential patterns. J Comput Sci 2006;2:318-21.
17. Sun G, Cong G, Liu X, et al. Mining sequential patterns and tree patterns to detect erroneous sentences. Proceedings of the 22nd Conference on Artificial Intelligence (AAAI 2007). Vancouver, British Columbia, Canada: Association for the Advancement of Artificial Intelligence, 2007:925-30.
18. Cong G, Wang L, Lin C.Y, et al. Finding question-answer pairs from online forums. Proceedings of the 31st ACM Special Interest Group on Information Retrieval (SIGIR 2008). Singapore: ACM Special Interest Group on Information Retrieval, 2008:467-74.
19. Li D, Kipper-Schuler K, Savova G, et al. Conditional random fields and support vector machines for disorder named entity recognition in clinical texts. Proceedings of Natural Language Processing in Biomedicine (BioNLP 2008). Columbus, OH, USA: Association for Computational Linguistics, 2008:94-5.
20. Settles B. Biomedical named entity recognition using conditional random fields and rich feature sets. Proceedings of Natural Language Processing in Biomedical Applications (NLPBA 2004). Barcelona, Spain: Association for Natural Language Processing in Biomedical Applications, 2004:104-7.
21. Uzuner O, Solti I, Cadag E. Extracting medication information from clinical text. J Am Med Inform Assoc 2010;17:514-18.
22. Uzuner O, South BR, Shen SY, et al. 2010 I2B2/VA challenge on concepts, assertions, and relations in clinical text. J Am Med Inform Assoc 2011;18: 552-6.
23. Partrick J, Li M. High accuracy information extraction of medication information from clinical notes: 2009 I2B2 medication extraction challenge. J Am Med Inform Assoc 2010;17:524-7.
24. Li Z, Cao Y, Antieau L. Extracting medication information from patient discharge summaries. Proceedings of the Third I2B2 Workshop on Challenges in Natural Language Processing for Clinical Data 2009. Washington DC, USA, 2006.
25. De Bruiji 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.
26. Jay N, Herengt G, Albuisson E, et al. Sequential pattern mining and classification of patient path. MEDINFO 2004:1667.
27. Yang WS, Hwang SY. A process-mining framework for the detection of healthcare fraud and abuse. Expert Syst Appl 2006;31:56-68.
28. Hanauer DA Zheng K. Detecting workflow changes after a CPOE implementation: a sequential pattern analysis approach. AMIA Annu Symp Proc 2008:963.
29. Zheng K, Padman R, Johnson MP, et al. An interface-driven analysis of user interactions with an electronic health records system. J Am Med Inform Assoc 2009;16:228-37.
30. Mani I, Verhagen M, Wellner B, et al. Machine Learning of Temporal Relations. Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics (ACL 2006). Sydney, Australia, 2006:753-60.
31. Schilder F, Katz G, Pustejovsky J. Annotating, extracting and reasoning about time and events. Lecture Notes Comput Sci 2007;4795:1-6.
32. Zhou L, Friedman C, Parsons S, et al. System architecture for temporal information extraction, representation and reasoning in clinical narrative reports. AMIA Annu Symp Proc 2005:869.
33. Hripcsak G, Elhadad N, Chen YH, et al. Using empiric semantic correlation to interpret temporal assertions in clinical texts. J Am Med Inform Assoc 2009;16:220-7.
34. Jung H, Allen J, Blaylock N, et al. Building timelines from narrative clinical records: initial results based-on deep natural language understanding. Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (ACL 2011). Portland, Oregon, USA, 2011:146.
35. Bramsen P, Deshpande P, Lee YK, et al. Finding temporal order in discharge summaries. AMIA Annu Symp Proc 2006:81.
36. 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;6:30-8.
37. Schmid H. Probabilistic part-of-speech tagging using decision trees. Proceedings of the International Conference on New methods in Language Processing (NeMLaP 1994). Manchester, 1994:44-9.
38. SharpNLP tools. http://sharpnlp.codeplex.com/
39. Savova GK, Chapman WW, Zheng J, et al. Anaphoric relations in the clinical narrative: corpus creation. J Am Med Inform Assoc 2011;18:459-65.
40. Pestian J. Emotional Assignment. I2B2vacincinnati-2011-challenge-I2B2vatrack- 2@googlegroups.com
41. Duda RO, Hart PE, Stork DG. Pattern Classification. 2nd edn. New York: John Wiley & Sons, Inc, 2003:389-90.