Community doctor scheduling method and system based on deep learning
A technology of deep learning and deep learning network, applied in neural learning methods, biological neural network models, healthcare resources or facilities, etc., can solve problems such as low efficiency of risk identification and untimely material reserves
Active Publication Date: 2020-02-04
重庆特斯联智慧科技股份有限公司
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[0004] In view of this, the purpose of this application is to propose a community doctor scheduling method and system based on deep learning, improve the level of community doctor scheduling identification, and solve the technical problems of low risk identification efficiency and untimely material reserves in the current flood control management process
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[0043] The application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain related inventions, not to limit the invention. It should also be noted that, for the convenience of description, only the parts related to the related invention are shown in the drawings.
[0044] It should be noted that, in the case of no conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and embodiments.
[0045] figure 1 A flowchart showing a method for scheduling community doctors based on deep learning according to an embodiment of the present invention. Such as figure 1 As shown, the deep learning-based community doctor scheduling method includes:
[0046] Step S11. Construct a...
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The embodiment of the invention provides a community doctor scheduling method and system based on deep learning. The method comprises the following steps: constructing a community doctor feature set according to professional features and visit features of community doctors; constructing a community patient feature set according to community patient traffic features and disease features; constructing a doctor-patient matching deep learning network and a traffic matching deep learning network based on the community doctor feature set and the community patient feature set; collecting real-time community doctor scheduling data, importing the real-time community doctor scheduling data into the deep learning network, and training a community doctor scheduling model; and performing community doctor scheduling according to the community doctor scheduling model. By combining the community doctor scheduling method and the deep learning characteristics, the community doctor scheduling efficiencyis improved.
Description
technical field [0001] This application relates to the field of deep learning, in particular to a method and system for scheduling community doctors based on deep learning. Background technique [0002] Community doctors are an important part of community health services. Community doctors focus on residents' health, not only performing daily diagnosis and health services, but also performing emergency treatment and inspections. China is a country with very tight medical resources. Community doctors, as grassroots medical workers, are even more in short supply. Moreover, with the acceleration of urbanization in China, the problem of urban traffic congestion has become increasingly prominent, and many patients often miss the best time for treatment due to the delay in the doctor's arrival. How to quickly, scientifically and accurately dispatch community doctors has become an urgent problem to be solved. [0003] Deep learning is a branch of machine learning, and its motiva...
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IPC IPC(8): G16H40/20G06N3/04G06N3/08
CPCG06N3/04G06N3/08G16H40/20
Inventor 孙斌董承利
Owner 重庆特斯联智慧科技股份有限公司