Multimedia Tools and Applications

, Volume 76, Issue 8, pp 10555–10573 | Cite as

Automatic extraction and visualization of semantic relations between medical entities from medicine instructions

  • Maofu Liu
  • Li Jiang
  • Huijun Hu


Recent years have witnessed the rapid development and tremendous research interests in healthcare domain. The health and medical knowledge can be acquired from many sources, such as professional health providers, health community generated data and textual descriptions of medicines. This paper explores the classification and extraction of semantic relation between medical entities from the unstructured medicine Chinese instructions. In this paper, three kinds of textual features are extracted from medicine instruction according to the nature of natural language texts. And then, a support vector machine based classification model is proposed to categorize the semantic relations between medical entities into the corresponding semantic relation types. Finally, the extraction algorithm is utilized to obtain the semantic relation triples. This paper also visualizes the semantic relations between medical entities with relationship graph for their future processing. The experimental results show that the approach proposed in this paper is effective and efficient in the classification and extraction of semantic relations between medical entities.


Semantic relation Medical entity Classification model Extraction algorithm Semantic relation triple Semantic relationship graph 



The work presented in this paper is partially supported by the National Natural Science Foundation of China under Grant No. 61100133 and the Major Projects of National Social Science Foundation of China under Grant No. 11&ZD189.


  1. 1.
    Abacha A, Zweigenbaum P (2011) Automatic extraction of semantic relations between medical entities: a rule based approach. J Biomed Semant 2(S-5):S4. doi: 10.1186/2041-1480-2-S5-S4 CrossRefGoogle Scholar
  2. 2.
    Al-Yahya M, Aldhubayi L, Al-Malak S (2014) A pattern-based approach to semantic relation extraction using a seed ontology. Proceedings of IEEE International Conference on Semantic Computing, 96–99Google Scholar
  3. 3.
    Chang C, Lin C (2011) LIBSVM: a library for support vector machines. J ACM Trans Intell Syst Technol 2(3):27Google Scholar
  4. 4.
    Chang X, Yang Y, Xing E, Yu Y (2015) Complex event detection using semantic saliency and nearly-isotonic SVM. Proceedings of the 32nd International Conference on Machine Learning, 1348–1357Google Scholar
  5. 5.
    Chen E, Hripcsak G, Xu H, Markatou M, Friedman C (2008) Automated acquisition of disease-drug knowledge from biomedical and clinical documents: an initial study. J Am Med Inform Assoc 15(1):87–98CrossRefGoogle Scholar
  6. 6.
    Claessen J, van Wijk J (2011) Flexible linked axes for multivariate data visualization. IEEE Trans Vis Comput Graph 17(12):2310–2316CrossRefGoogle Scholar
  7. 7.
    de Bruijn B, Cherry C, Kiritchenko S, Martin J, Zhu X (2011) Machine-learned solutions for three stages of clinical information extraction: the state of the art at i2b2 2010. J Am Med Inform Assoc 18(5):557–562CrossRefGoogle Scholar
  8. 8.
    Embarek M, Ferret O (2008) Learning patterns for building resources about semantic relations in the medical domain. Proceedings of The 6th international conference on Language Resources and Evaluation.
  9. 9.
    Kamsu-Foguem B, Tchuenté-Foguem G, Foguem C (2014) Using conceptual graphs for clinical guidelines representation and knowledge visualization. J Inf Syst Front 16(4):571–589CrossRefGoogle Scholar
  10. 10.
    Kolb J, Reichert M, Weber B (2012) Using concurrent task trees for stakeholder-centered modeling and visualization of business processes. Proceedings of 4th International Conference of Education and Industrial Developments, 237–251Google Scholar
  11. 11.
    Maeda Y, Yoon S (2013) A meta-analysis on gender differences in mental rotation ability measured by the Purdue spatial visualization tests: visualization of rotations (PSVT: R). J Educ Psychol Rev 25(1):69–94CrossRefGoogle Scholar
  12. 12.
    Nie L, Akbari M, Li T, Chua T (2014) A joint local–global approach for medical terminology assignment. Proc Med Inf Retr Workshop SIGIR 2014:24–27Google Scholar
  13. 13.
    Nie L, Li T, Akbari M, Shen J, Chua T (2014) Wenzher: comprehensive vertical search for healthcare domain. Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, 1245–1246Google Scholar
  14. 14.
    Nie L, Wang M, Zhang L, Yan S, Bo Z, Chua T (2014) Disease inference from health-related questions via sparse deep learning. IEEE Trans Knowl Data Eng 27(8):2017–2119Google Scholar
  15. 15.
    Nie L, Zhao Y, Akbari M, Shen J, Chua T (2013) Bridging the vocabulary gap between health seekers and healthcare knowledge. IEEE Trans Knowl Data Eng 27(2):396–409CrossRefGoogle Scholar
  16. 16.
    Quan C, Wang M, Ren F (2014) An unsupervised text mining method for relation extraction from biomedical literature. PLoS ONE 9(7), e102039CrossRefGoogle Scholar
  17. 17.
    Rink B, Harabagiu S, Roberts K (2011) Automatic extraction of relations between medical concepts in clinical texts. J Am Med Inform Assoc 18(5):594–600CrossRefGoogle Scholar
  18. 18.
    Roberts A, Gaizauskas R, Hepple M (2008) Extracting clinical relationships from patient narratives. Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing, Association for Computational Linguistics, 10–18Google Scholar
  19. 19.
    Song S, Heo G, Kim H, Jung H, Kim Y, Song M (2014) Grounded feature selection for biomedical relation extraction by the combinative approach. Proceedings of the ACM 8th International Workshop on Data and Text Mining in Bioinformatics, 29–32Google Scholar
  20. 20.
    Uzuner Ö, South B, Shen S, DuVall S (2011) 2010 i2b2/VA challenge on concepts, assertions, and relations in clinical texts. J Am Med Inform Assoc 18(5):552–556CrossRefGoogle Scholar
  21. 21.
    Venkatesan P, Mullai M (2014) Visualization of breast cancer data by SOM component planes. Int J Sci Technol 3(2):127–134Google Scholar
  22. 22.
    Wang X, Chused A, Elhadad N, Friedman C, Markatou M (2008) Automated knowledge acquisition from clinical narrative reports. Proceeding of AMIA Annual Symposium. American Medical Informatics Association, 783–787Google Scholar
  23. 23.
    Wang J, Yu Q, Guan Y, Jiang Z (2014) An overview of research on electronic medical record oriented named entity recognition and entity relation extraction. J Autom Sin 40(8):1537–1562Google Scholar
  24. 24.
    Yan Y, Liu G, Ricci E, Sebe N (2013) Multi-task linear discriminant analysis for multi-view action recognition. Proceeding of the 20th IEEE International Conference on Image Processing, 2842–2846Google Scholar
  25. 25.
    Yan Y, Ricci E, Liu G, Sebe N (2015) Egocentric daily activity recognition via multitask clustering. IEEE Trans Image Process 24(10):2984–2995MathSciNetCrossRefGoogle Scholar
  26. 26.
    Yan Y, Ricci E, Subramanian R, Lanz O, Sebe N (2013) No matter where you are: flexible graph-guided multi-task learning for multi-view head pose classification under target motion. Proceeding of 2013 I.E. International Conference on Computer Vision, 1177–1184Google Scholar
  27. 27.
    Yan Y, Yang Y, Meng D, Liu G, Tong W, Hauptmann A, Sebe N (2015) Event oriented dictionary learning for complex event detection. IEEE Trans Image Process 24(6):1867–1878MathSciNetCrossRefGoogle Scholar
  28. 28.
    Yang Y, Lai P, Tsai R (2014) A hybrid system for temporal relation extraction from discharge summaries. Technologies and Applications of Artificial Intelligence, Springer International Publishing, 379–386Google Scholar
  29. 29.
    Zhang L, Gao Y, Xia Y, Dai Q, Li X (2015) A fine-grained image categorization system by cellet-encoded spatial pyramid modeling. IEEE Trans Ind Electron 62(1):564–571CrossRefGoogle Scholar
  30. 30.
    Zhang L, Gao Y, Xia Y, Lu K, Shen J, Ji R (2014) Representative discovery of structure cues for weakly-supervised image segmentation. IEEE Trans Multimed 16(2):470–479CrossRefGoogle Scholar
  31. 31.
    Zhang L, Han Y, Yang Y, Song M, Yan S, Tian Q (2013) Discovering discriminative graphlets for aerial image categories recognition. IEEE Trans Image Process 22(12):5071–5084MathSciNetCrossRefGoogle Scholar
  32. 32.
    Zhang L, Song M, Liu X, Bu J, Chen C (2013) Fast multi-view segment graph kernel for object classification. Signal Process 93(6):1597–1607CrossRefGoogle Scholar
  33. 33.
    Zhang L, Yang Y, Gao Y, Yu Y, Wang C, Li X (2014) A probabilistic associative model for segmenting weakly supervised images. IEEE Trans Image Process 23(9):4150–4159MathSciNetCrossRefGoogle Scholar
  34. 34.
    Zhu J, Nie Z, Liu X, Zhang B, Wen J (2009) StatSnowball: a statistical approach to extracting entity relationships. Proceedings of the 18th International Conference on World Wide Web, 101–110Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.College of Computer Science and TechnologyWuhan University of Science and TechnologyWuhanChina
  2. 2.Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial SystemWuhanChina

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