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2019 | OriginalPaper | Buchkapitel

Geriatric Disease Reasoning Based on Knowledge Graph

verfasst von : Shaobin Feng, Huansheng Ning, Shunkun Yang, Dongmei Zhao

Erschienen in: Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health

Verlag: Springer Singapore

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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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Titel: Geriatric Disease Reasoning Based on Knowledge Graph
verfasst von: Shaobin Feng
Huansheng Ning
Shunkun Yang
Dongmei Zhao
Verlag: Springer Singapore
Buch: Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health
Print ISBN: 978-981-15-1924-6

Electronic ISBN: 978-981-15-1925-3

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-981-15-1925-3_33

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