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  4. Vol 4, Issue 1, 2021

Research on Entity Recognition and Knowledge Graph Construction Based on Tcm Medical Records

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DOI: 10.23977/jaip.2020.040105 | Downloads: 49 | Views: 1628

Author(s)

Yanling YANG 1, Xinyu Zhong 1, Lina Xu 1

Affiliation(s)

1 Information Engineering Institution of Gansu University of Chinese Medicine, Lanzhou, Gansu, 730000, China

Corresponding Author

Yan LI

ABSTRACT

Traditional Chinese medicine (TCM) medical records contain valuable medical information, and are important resources for personalized knowledge analysis, auxiliary diagnosis and treatment, clinical decision support, and drug to use pattern mining of famous TCM doctors. As an effective and novel knowledge management technology, knowledge graph can provide a new way for the inheritance and development of TCM. Constructing medical knowledge graph can potentially help to discover knowledge from clinical data, assist clinical decision-making and personalized treatment recommendation. However, the construction of TCM knowledge graph is still mainly based on structured data, and unstructured texts such as medical records, literature and electronic medical records urgently need to be extracted for mining and analysis. Aiming at the difficulties of word segmentation, entity variety and ambiguity in TCM medical records, this paper proposes a named entity recognition method of deep learning hybrid model based on two-way long-term memory (BILSTM) network and conditional random field (CRF); then by analyzing the process of TCM diagnosis and treatment, the core concepts of TCM are extracted and the ontology layer is constructed; finally, the knowledge graph is constructed by Neo4j, which can provide retrieval, visualization and other functions to help the learning and sharing of TCM knowledge.

KEYWORDS

Knowledge graph, Tcm medical records, Name entity recognition, Relation extraction

CITE THIS PAPER

Yanling YANG, Yan LI, Xinyu Zhong, Lina Xu. Research on Entity Recognition and Knowledge Graph Construction Based on Tcm Medical Records. Journal of Artificial Intelligence Practice (2021) Vol. 4: 39-53. DOI: http://dx.doi.org/10.23977/jaip.2020.040105.

REFERENCES

[1] Weng H, Liu Z, Yan S, et al. A Framework for automated knowledge graph construction towards Traditional Chinese Medicine, Proceedings of the International Conference on Health Information Science, F, 2017.
[2] Yu T, Li J, Yu Q, et al. Knowledge Graph for TCM Health Preservation: Design, Construction and Applications. Artificial Med, 2017,77,48-52.
[3] Sang S. A Knowledge Graph based Bidirectional Recurrent Neural Network Method for Literature-Based Discovery; Proceedings of the 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), F, 2018.
[4] Xie Y, Hu L, Chen X, et al. Auxiliary Diagnosis based on the Knowledge Graph of TCM Syndrome [J]. Computers, Materials and Continua, 2020, 65(1): 481-94.
[5] Collobert R, Weston J, Karlen M, et al. Natural language processing (almost) from scratch[J]. Journal of Machine Learning Research, 2011, 12(1):2493-2537.
[6] Lample G, Ballesteros M, Suramanans S, et al.[C] Neural architectures for name identity recognition//Proc of Conference of the North American Chapter of the Association for Computational Linguistics:HumanLanguageTechnologies.2016:260-270.
[7] L.R. JIA, J. LIU, T. YU, Y. DONG, L. ZHU, B. GAO, and L.H. LIU, “Construction of traditional Chinese medicine knowledge graph,” Journal of Medical Informatics, vol. 36, pp. 51-59, August 2015.
[8] T. RUAN, C.L. SUN, H.F. WANG, Z.J. FANG, and Y.C. YIN, “Construction of Traditional Chinese medicine knowledge graph and its application,” Journal of Medical Informatics, vol. 37, pp. 8-13, April 2016.
[9] Wan H, Marie-francine M, Walter L, et al. Extracting relations from traditional Chinese medicine literature via heterogeneous entity networks [J]. Journal of the American Medical Informatics Association Jamia, 2016, (2): 356.
[10] BIN JI,RUI lIU, SHA SHA LI,JIN TAO TANG, et al. A BILSTM-CRF Method to Chinese Electronic Medicine Record Named Entity Recognition[A].International Association of Applied Science and Engineering:2018:6.
[11] Dai Z,Wang X,Ni P, et al. Named recognition using BERT BILSTM CRF for Chinese Electronic Health Records[C]//2019 12th International Congress on Imagine and Signal Processing, Bio Medical Engineering and Informatics(CISP-BMEI),2019.
[12] PEI-LIN L, ZHEN-MING Y, WE-NBO T, et al. Medical Knowledge Extraction and Analysis from Electronic Medical Records Using Deep Learning [J]. Chinese medical sciences journal , 2019, 34(2):133-9.
[13] Xiu X, Qian Q, WU S. Construction of a Digestive System Tumor Knowledge Graph Based on Chinese Electronic Medical Records: Development and Usability Study [J]. JMIR Medical Informatics, 2020, 8(10): e18287.
[14] Peng Z, Song H, Zheng X, et al. Construction of hierarchical knowledge graph based on deep learning; proceedings of the 2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA), F, 2020 [C].
[15] ZHU, LING, GAO, et al. Knowledge graph for TCM health preservation: Design, construction, and applications [J]. Artificial Intelligence in Medicine, 2017, PP:48-52.
[16] Chai X. Diagnosis Method of Thyroid Disease Combining Knowledge Graph and Deep Learning [J]. IEEE Access, 2020, PP (99): 1-1.

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