Abstract
The lack of health care for ageing has become one of China’s most serious challgengs. The main work of this paper is building a database of a geriatric knowledge graph and proposing three inference rules based on Bayesian algorithm, which can effectively help the elderly to understand their health better and find out the abnormal condition as soon as possible. At the same time, it can assist doctors make auxiliary medical decisions and improve the cure rate. This article introduced a complete process of building a knowledge graph, from schema structure design to data acquisition, and processing the data until it fits the standard. Before applying to disease reasoning, we imported knowledge data into the Neo4j graph database to make full use of the inference flexibility and accuracy of the knowledge graph.
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References
National Bureau of Statistics of China: The 2018 Population Age Structure of China. http://data.stats.gov.cn/easyquery.htm?cn=C01&zb=A0301&sj=2018
Google: Introducing the Knowledge Graph: Things, Not Strings. https://googleblog.blogspot.com/2012/05/introducing-knowledge-graph-things-not.html
Hong, L.: The influence of aging population on China’s economy in the information society. In: 2010 2nd IEEE International Conference on Information Management and Engineering, pp. 264–267 (2010). https://doi.org/10.1109/ICIME.2010.5478053
Jiang, J., Li, X., Zhao, C., Guan, Y., Yu, Q.: Learning and inference in knowledge-based probabilistic model for medical diagnosis. Knowl.-Based Syst. 138, 58–68 (2017)
Kabboord, A.D., Van Eijk, M., Buijck, B.I., Koopmans, R.T., van Balen, R., Achterberg, W.P.: Comorbidity and intercurrent diseases in geriatric stroke rehabilitation: a multicentre observational study in skilled nursing facilities. Eur. Geriatr. Med. 9(3), 347–353 (2018)
Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: Twenty-Ninth AAAI Conference on Artificial Intelligence (2015)
Maranesi, E., et al.: A stereophotogrammetric-based method to assess spatio-temporal gait parameters on healthy and Parkinsonian subjects. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 5501–5504 (2015). https://doi.org/10.1109/EMBC.2015.7319637
Mohammed, H.B.M., Ibrahim, D., Cavus, N.: Mobile device based smart medicationreminder for older people with disabilities. Qual. Quant. 52(2), 1329–1342 (2018). https://doi.org/10.1007/s11135-018-0707-8
Nickel, M., Murphy, K., Tresp, V., Gabrilovich, E.: A review of relational machine learning for knowledge graphs. Proc. IEEE 104(1), 11–33 (2016). https://doi.org/10.1109/JPROC.2015.2483592
OpenKG.CN: Chinese symptom library. http://openkg.cn/dataset/symptom-in-chinese
Ruan, T., Huang, Y., Liu, X., Xia, Y., Gao, J.: QAnalysis: a question-answer driven analytic tool on knowledge graphs for leveraging electronic medical records for clinical research. BMC Med. Inform. Decis. Making 19(1), 82 (2019)
Stark, B., Knahl, C., Aydin, M., Samarah, M., Elish, K.O.: Betterchoice: a migraine drug recommendation system based on neo4j. In: 2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA), pp. 382–386. IEEE (2017)
Urošević, V., Paolini, P., Tatsiopoulos, C.: Configurable interactive environment for hybrid knowledge- and data-driven geriatric risk assessment. In: 2017 25th International Conference on Software, Telecommunications and Computer Networks (SoftCOM), pp. 1–7 (2017). https://doi.org/10.23919/SOFTCOM.2017.8115520
Vukotic, A., Watt, N., Abedrabbo, T., Fox, D., Partner, J.: Neo4j in Action. Manning Publications Co. (2014)
Xiong, W., Zeng, Z., Xie, Y., Nie, B., Zhou, X.: Study on taboo knowledge map of Chinese patent medicine compatibility. In: AIP Conference Proceedings, p. 020052. AIP Publishing (2019)
Yu, P., Liu, X., Wang, J.: Geriatric medicine in China: the past, present, and future. Aging Med. 1(1), 46–49 (2018)
Zhou, J., Cui, G., Zhang, Z., Yang, C., Liu, Z., Sun, M.: Graph neural networks: a review of methods and applications. arXiv preprint arXiv:1812.08434 (2018)
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Feng, S., Ning, H., Yang, S., Zhao, D. (2019). Geriatric Disease Reasoning Based on Knowledge Graph. In: Ning, H. (eds) Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health. CyberDI CyberLife 2019 2019. Communications in Computer and Information Science, vol 1138. Springer, Singapore. https://doi.org/10.1007/978-981-15-1925-3_33
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DOI: https://doi.org/10.1007/978-981-15-1925-3_33
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