Advertisement

Evidence-Based Treatment of Medical Guideline

  • Pingfang Tian
  • Zhonghua ZhuEmail author
  • Zhisheng Huang
Conference paper
  • 589 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 480)

Abstract

Medical guidelines are recommendations on the appropriate treatment and care of people with specific diseases and conditions. Evidence-based medical guidelines are the document or recommendations which have been annotated with their relevant medical evidences, namely research findings from medical publications. We have observed the fact that there exist significant amount of medical guidelines have not yet annotated with relevant medical evidences, which becomes even more serious in the Chinese medical guidelines. In this paper, we propose an approach of evidence process of medical guidelines, such that we can find relevant evidences for those non-evidence-based medical guidelines. We develop a system called Link2Pubmed, which can retrieve the text which is described with a natural language and get the corresponding medical evidences. We use the word segmentation and part-of-speech tagging tools in natural language processing (NLP) to extract the keywords, and then translate them into corresponding English concepts in SNOMED CT, a well-known medical ontology. This system is an attempt to solve the existing problems in Chinese medical guidelines, which lack the annotations of relevant evidences.

Keywords

Medical guideline NLP SNOMED CT PubMed Link2Pubmed 

Notes

Acknowledgement

This work was partially supported by a grant from the NSF (Natural Science Foundation) of China under grant number 60803160 and 61272110, the Key Projects of National Social Science Foundation of China under grant number 11&ZD189, and it was partially supported by a grant from NSF of Hubei Prov. of China under grant number 2013CFB334. It was partially supported by NSF of educational agency of Hubei Prov. under grant number Q20101110, and the State Key Lab of Software Engineering Open Foundation of Wuhan University under grant number SKLSE2012-09-07.

References

  1. 1.
    Xiao-wei, J., Zhu-ming, J.: The present status of Chinese clinical practice guidelines. Chin. J. Clin. Nutr. 18(6), 327–329 (2010)Google Scholar
  2. 2.
    Jiang, Z.M., Wilmore, D.W., Wang, X.R., et al.: Randomized clinical trial of intravenous soybean oil alone versus soybean oil plus fish oil emulsion after gastrointestinal cancer surgery. Br. J. Surg. 97(6), 804–809 (2010)CrossRefGoogle Scholar
  3. 3.
    He, L., Du, X.L.: Retrieval research of evidence based clinical practice guidelines. J. Clin. Rehabilitative Tissue Eng. Res. 11(40), 8173–8177 (2007)Google Scholar
  4. 4.
    Mast, K.R., Salama, M., Silverman, G.K., Arnold, R.M.: End-of-life content in treatment guidelines for life-limiting diseases. J. Palliat. Med. 7(6), 754–773 (2004)CrossRefGoogle Scholar
  5. 5.
    Kennedy, S., Bergqvist, A., Chapron, C., D’Hogghe, T., et al.: ESHRE guideline for the diagnosis and treatment of endometriosis. Hum. Reprod. 20(6), 2698–2704 (2005)CrossRefGoogle Scholar
  6. 6.
    Burton, M.J., Couch, M.E.: Rosenfeld. R.M.: Extracts from the cochrane library homeopathic medicines for adverse effects of cancer treatments. Otolaryngol. Head Neck Surg. 141(2), 162–165 (2009)CrossRefGoogle Scholar
  7. 7.
    Collobert, R., Weston, J.: A unified architecture for natural language processing: deep neural networks with multitask learning. In: Proceeding of the 25th International Conference on Machine, pp. 160–167 (2008)Google Scholar
  8. 8.
    Chopra, A., Prashar, A., Sain, C.: Natural language processing. Int. J. Enhancements Emerg. Eng. Res. 1(4), 131–134 (2013)Google Scholar
  9. 9.
    Elkin, P.L., Brown, S.H.: Evaluation of the content coverage of SNOMED CT: ability of SNOMED clinical terms to represent clinical problem lists. Mayo Clin. Proc. 81(6), 741–748 (2006)CrossRefGoogle Scholar
  10. 10.
    Wasserman, H., Wang, J.: An applied evaluation of SNOMED CT as a clinical vocabulary for the computerized diagnosis and problem list. AMIA Annu. Symp. Proc. 2003, 699–703 (2003)Google Scholar
  11. 11.
    沈锡宾, 吕小东, 郝秀原, 孙静, 汪谋岳, 郭利劭. PubMed Central简介及其对期刊的评估和收录. 中国科技期刊研究 17(5), 866–868 (2006) (XiBin, S., XiaoDong, L., XiuYuan, H., Jing, S., MouYue, W., LiShao, G.: PubMed central introduction and a review and record of journal. Chin. J. Sci. Tech. Periodicals 17(5), 866–868 (2006))Google Scholar
  12. 12.
    Jiang, W., Huang, L., Liu, Q., Lü, Y.: A cascaded linear model for joint chinese word segmengation and part-of-speech tagging. In: Proceeding of ACL, pp. 897–904 (2008)Google Scholar
  13. 13.
    Jiang, W., Mi, H., Liu, Q.: Word lattice reranking for Chinese word segmentation and part-of-speech tagging. In: Proceedings of the 22nd International Conference on Computational Linguistics, pp. 385–392 (2008)Google Scholar
  14. 14.
    Zhang, H., Yu, H., Xiong, D., Liu, Q.: HHMM-based Chinese lexical analyzer ICTCLAS. SIGHAN ‘03 Proceedings of the Second SIGHAN Workshop on Chinese Language Processing, pp. 184–187 (2003)Google Scholar
  15. 15.
    夏天, 樊孝忠, 刘林. 利用JNI实现ICTCLAS系统的Java调用. 计算机应用 24(Z2), 177–178 (2004). (Xia, T., Fan, X., Liu, L.: Java calls using JNI ICTCLAS system. Comput. Appl. 24(Z2), 177–178 (2004)Google Scholar
  16. 16.
    Ceusters, W., Smith, B., Kumar, A.: Dhaen, A.: Ontology-based error detection in SNOMED-CT. In: Proceedings of MEDINFO 482–486 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.College of Computer Science and TechnologyWuhan University of Science and TechnologyWuhanPeople’s Republic of China
  2. 2.Hubei Province Key Laboratory of Intelligent Information Processing and Realtime Industrial SystemWuhanPeople’s Republic of China
  3. 3.Department of Computer ScienceVrije University of AmsterdamAmsterdamThe Netherlands

Personalised recommendations