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

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Abstract

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.

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Notes

  1. http://www.pubmed.com

  2. http://www.chu-rouen.fr/cismef

  3. http://www.healthonnet.org

  4. The English versions for all the examples are obtained by the Google’ s translator.

  5. http://gephi.github.io/

  6. http://nlp.stanford.edu/software/tagger.shtml

  7. http://www.opengeospatial.org/standards/gml/

  8. http://www.json.org/

  9. http://echarts.baidu.com/

  10. http://www.j1.com/ http://top.chinaz.com/site_www.yiwang.cn.html

  11. http://www.csie.ntu.edu.tw/~cjlin/libsvm/

References

  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

    Article  Google Scholar 

  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, 96–99

  3. Chang C, Lin C (2011) LIBSVM: a library for support vector machines. J ACM Trans Intell Syst Technol 2(3):27

    Google Scholar 

  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, 1348–1357

  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):87–98

    Article  Google Scholar 

  6. Claessen J, van Wijk J (2011) Flexible linked axes for multivariate data visualization. IEEE Trans Vis Comput Graph 17(12):2310–2316

    Article  Google Scholar 

  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):557–562

    Article  Google Scholar 

  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. http://www-ist.cea.fr/publicea/exl-doc/200700004984.pdf

  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):571–589

    Article  Google Scholar 

  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, 237–251

  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):69–94

    Article  Google Scholar 

  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 2014:24–27

    Google Scholar 

  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, 1245–1246

  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):2017–2119

    Google Scholar 

  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):396–409

    Article  Google Scholar 

  16. Quan C, Wang M, Ren F (2014) An unsupervised text mining method for relation extraction from biomedical literature. PLoS ONE 9(7), e102039

    Article  Google Scholar 

  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):594–600

    Article  Google Scholar 

  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, 10–18

  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, 29–32

  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):552–556

    Article  Google Scholar 

  21. Venkatesan P, Mullai M (2014) Visualization of breast cancer data by SOM component planes. Int J Sci Technol 3(2):127–134

    Google Scholar 

  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, 783–787

  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):1537–1562

    Google Scholar 

  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, 2842–2846

  25. Yan Y, Ricci E, Liu G, Sebe N (2015) Egocentric daily activity recognition via multitask clustering. IEEE Trans Image Process 24(10):2984–2995

    Article  MathSciNet  Google Scholar 

  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, 1177–1184

  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):1867–1878

    Article  MathSciNet  Google Scholar 

  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, 379–386

  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):564–571

    Article  Google Scholar 

  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):470–479

    Article  Google Scholar 

  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):5071–5084

    Article  MathSciNet  Google Scholar 

  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):1597–1607

    Article  Google Scholar 

  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):4150–4159

    Article  MathSciNet  Google Scholar 

  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, 101–110

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Acknowledgments

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.

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Authors and Affiliations

  1. College of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, 430065, China

    Maofu Liu, Li Jiang & Huijun Hu

  2. Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan, 430065, China

    Maofu Liu, Li Jiang & Huijun Hu

Authors
  1. Maofu Liu
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  2. Li Jiang
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  3. Huijun Hu
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Correspondence to Maofu Liu.

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Liu, M., Jiang, L. & Hu, H. Automatic extraction and visualization of semantic relations between medical entities from medicine instructions. Multimed Tools Appl 76, 10555–10573 (2017). https://doi.org/10.1007/s11042-015-3093-4

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  • DOI: https://doi.org/10.1007/s11042-015-3093-4

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