Abstract
The medical knowledge graph describes medical entities and relations in a structured form, which is one of the most important representations for integrating massive medical resources. It is widely used in intelligent question-answering, clinical decision support, and other medical services. The key to building a high-quality medical knowledge graph is the standardization of named entities and relations. However, the research in annotation and specification of named entities and relations is limited. Based on the current research on the medical annotated corpus, this paper establishes an annotation scheme and specification for named entities and relations centered on diseases under the guidance of physicians. The specification contains 11 medical concepts and 12 medical relations. Medical concepts include the diagnosis, treatment, and prognosis of diseases. Medical relations focus on relation types between diseases and medical concepts. In accordance with the specification, a new Chinese medical annotated corpus of high consistency is constructed.
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References
Chen, Y., Liu, Z.: The rise of mapping knowledge domain. Stud. Sci. Sci. 23, 149–154 (2005). [In Chinese]
Martín, R.W.: The use of medical terminology. A thesaurus. Medical subject headings. Aten Primaria 23, 548–552 (1999)
Quan, H., Sundararajan, V., Halfon, P., et al.: Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med. Care 43, 1130–1139 (2005)
Li, M.: Basic concept and application of clinical pathway. Chin. J. Nurs. 45, 59–61 (2010). [In Chinese]
Carlson, A., Betteridge, J., Kisiel, B., et al.: Toward an architecture for never, ending language learning. In: Twenty, fourth Aaai Conference on Artificial Intelligence (2010)
Stearns, M.Q., Price, C., Spackman, K.A., et al.: SNOMED clinical terms: overview of the development process and project status. In: Proceedings of AMIA Annual Symposium, vol. 8, p. 662 (2001)
Uzuner, O., South, B.R., Shen, S., et al.: 2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text. J. Am. Med. Inform. Assoc. 18, 552–556 (2011)
Mizuki, M., Yoshinobu, K., Tomoko, O., Mai, M., Aramaki, E.: Overview of the NTCIR-10 MedNLP task. In: Proceedings of the NTCIR-10 (2013)
Lei, J., Tang, B., Lu, X., et al.: Research and applications: a comprehensive study of named entity recognition in Chinese clinical text. J. Am. Med. Inform. Assoc. Jamia 21, 808 (2014)
Wang, H., Zhang, W., Zeng, Q., et al.: Extracting important information from Chinese operation notes with natural language processing methods. J. Biomed. Inform. 48, 130–136 (2014)
Yang, J.F., Guan, Y., He, B., et al.: Corpus construction for named entities and entity relations on Chinese electronic medical records. J. Softw. 27, 2725–2746 (2016). [In Chinese]
Xia, F., Yetisgen Yildiz, M.: Clinical corpus annotation: challenges and strategies. In: Proceedings of the 3rd Workshop on Building and Evaluating Resources for Biomedical Text Mining of the International Conference on Language Resources and Evaluation (LREC), pp. 32–39 (2012)
Acknowledgments
This research is supported by the National Social Science Fund of China (No. 18ZDA315), the Key Scientific Research Program of Higher Education of Henan (No. 20A520038), the science and technology project of Science and Technology Department of Henan Province (No. 192102210260), and the international cooperation project of Science and Technology Department of Henan Province (No. 172102410065).
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Yue, D., Zhang, K., Zhuang, L., Zhao, X., Byambasuren, O., Zan, H. (2020). Annotation Scheme and Specification for Named Entities and Relations on Chinese Medical Knowledge Graph. In: Hong, JF., Zhang, Y., Liu, P. (eds) Chinese Lexical Semantics. CLSW 2019. Lecture Notes in Computer Science(), vol 11831. Springer, Cham. https://doi.org/10.1007/978-3-030-38189-9_58
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DOI: https://doi.org/10.1007/978-3-030-38189-9_58
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