@inproceedings{cd14d5c7cefa43f7be4db22ddb6f0526,
title = "Geriatric disease reasoning based on knowledge graph",
abstract = "The lack of health care for ageing has become one of China{\textquoteright}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.",
keywords = "Geriatric disease, Knowledge graph, Neo4j, Reasoning algorithm",
author = "Shaobin Feng and Huansheng Ning and Shunkun Yang and Dongmei Zhao",
note = "Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd. 2019.; 3rd International Conference on Cyberspace Data and Intelligence, Cyber DI 2019, and the International Conference on Cyber-Living, Cyber-Syndrome, and Cyber-Health, CyberLife 2019 ; Conference date: 16-12-2019 Through 18-12-2019",
year = "2019",
doi = "10.1007/978-981-15-1925-3\_33",
language = "英语",
isbn = "9789811519246",
series = "Communications in Computer and Information Science",
publisher = "Springer",
pages = "452--465",
editor = "Huansheng Ning",
booktitle = "Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health - International 2019 Cyberspace Congress, CyberDI and CyberLife, Proceedings",
address = "德国",
}