Extracting and Normalizing Temporal Expressions in Clinical Data Requests from Researchers

详细信息    查看全文

• 作者：Tianyong Hao (23)
  Alex Rusanov (24)
  Chunhua Weng (23)
  
• 关键词：temporal expression extraction ; clinical request ; pattern learning
• 刊名：Lecture Notes in Computer Science
• 出版年：2013
• 出版时间：2013
• 年：2013
• 卷：8040
• 期：1
• 页码：52-65
• 全文大小：286KB
• 参考文献：1. Lonsdale, D.W., Tustison, C., Parker, C.G., Embley, D.W.: Assessing Clinical Trial Eligibility with Logic Expression Queries. Data & Knowledge Engineering?66(1), 3-7 (2008) CrossRef
  2. Hruby, G.W., Boland, M.R., et al.: Characterization of the Biomedical Query Mediation Process. In: Proc. of AMIA 2013 Clinical Research Informatics Summit, San Francisco, CA, March 18-22, pp. 89-3 (2013)
  3. Str?tgen, J., Gertz, M.: Heideltime: High Quality Rule-based Extraction and Normalization of Temporal Expressions. In: Proc. of the Workshop on Semantic Evaluation, pp. 321-24. ACL (2010)
  4. Pustejovsky, J., Verhagen, M.: Semeval-2010 Task 13: Evaluating Events, Time Expressions, and Temporal Relations (tempeval-2). In: Proc. of the Workshop on Semantic Evaluations, pp. 112-16. ACL (2009)
  5. Verhagen, M., Sauri, R., Caselli, T., Pustejovsky, J.: Semeval-2010 Task 13: Tempeval-2. In: Proc. of the Workshop on Semantic Evaluation, pp. 57-2. ACL (2010)
  6. Pustejovsky, P., Casta?o, J., et al.: Timeml: Robust Specification of Event and Temporal Expressions in Text. In: Proc. of the IWCS-5 Fifth International (2003)
  7. Sohn, S., Wagholikar, K., Li, D., et al.: Comprehensive Temporal Information Detection from Clinical Text: Medical Events, Time, and Tlink Identification. J. Am. Med. Inform. Assoc. (2013), doi:10.1136/amiajnl-2013-00162
  8. Tang, B., Wu., Y., et al.: A Hybrid System for Temporal Information Extraction from Clinical Text. J. Am. Med. Inform. Assoc. (2013), doi:10.1136/amiajnl-2013-001635
  9. Tao, C., He, Y., Poland, G., Chute, C., Yang, H.: Ontology-based Time Information Representation of Vaccine Adverse Events in Vaers for Temporal Analysis. Journal of Biomedical Semantics?3(13) (2012)
  10. Li, M., Patrick, J.: Extracting Temporal Information from Electronic Patient Records. In: Proc. of AMIA Annu. Symp. Proc., pp. 542-51 (2012)
  11. Galescu, L., Blaylock, N.: A Corpus of Clinical Narratives Annotated with Temporal Information. In: Proc. of International Health Informatics Symposium, pp. 715-20 (2012)
  12. Luo, Z., Johnson, S., Lai, A., Weng, C.: Extracting Temporal Constraints from Clinical Research Eligibility Criteria Using Conditional Random Fields. In: Proc. of AMIA Annual Symposium, pp. 843-52 (2011)
  13. Mani, I., Wilson, G.: Automating Temporal Annotation with Tarsqi. In: Proc. of 38th Annual Meeting of the ACL, pp. 69-6 (2000)
  14. Zhao, R., Do, Q., Roth, D.: A Robust Shallow Temporal Reasoning System. In: Proc. of NAACL-HLT Demo. (2012)
  15. Bird, S.: Nltk: the Natural Languagetoolkit. In: Proc. of the COLING/ACL 2006 Interactive Presentation Sessions, pp. 69-2 (2006)
  
• 作者单位：Tianyong Hao (23)
  Alex Rusanov (24)
  Chunhua Weng (23)
  
  23. Department of Biomedical Informatics, Columbia University, USA
  24. Department of Anesthesiology, Columbia University, USA
  

文摘

Automatic translation of clinical researcher data requests to executable database queries is instrumental to an effective interface between clinical researchers and “Big Clinical Data- A necessary step towards this goal is to parse ample temporal expressions in free-text researcher requests. This paper reports a novel algorithm called TEXer. It uses heuristic rule and pattern learning for extracting and normalizing temporal expressions in researcher requests. Based on 400 real clinical queries with human annotations, we compared our method with four baseline methods. TEXer achieved a precision of 0.945 and a recall of 0.858, outperforming all the baseline methods. We conclude that TEXer is an effective method for temporal expression extraction from free-text clinical data requests.