METHOD AND DEVICE FOR CONSTRUCTING MEDICAL KNOWLEDGE GRAPH AND ASSISTANT DIAGNOSIS METHOD

    公开(公告)号:US20180322954A1

    公开(公告)日:2018-11-08

    申请号:US15972121

    申请日:2018-05-05

    CPC classification number: G16H50/20 G06F17/278 G16H10/60 G16H50/70

    Abstract: The present invention discloses a method and a device for constructing a medical knowledge graph and an assistant diagnosis method. The assistant diagnosis method based on a medical knowledge graph comprises the steps of: acquiring complaint data and examination data of a patient and processing the data to obtain symptom entities and sign entities of the patient; searching, from the medical knowledge graph, disease entities associated with the symptom entities and the sign entities, calculating a posterior probability of each disease entity separately under a set of its corresponding symptom entities and sign entities; and outputting a disease entity with a maximum posterior probability as well as data corresponding to its associated nodes. The present invention provides an intelligent assistant diagnosis for clinical medicine to reduce the work burden of medical staff, relieve the medical pressure and reduce medical accidents.

    TASK SCHEDULING SYSTEM AND METHOD SUITABLE FOR MOBILE HEALTH

    公开(公告)号:US20180136974A1

    公开(公告)日:2018-05-17

    申请号:US15809993

    申请日:2017-11-10

    CPC classification number: G06F9/4881 G06F19/325 G06F19/3418 G16H40/20

    Abstract: The present invention discloses a task scheduling system and method suitable for mobile health, the characteristics thereof including: a mobile health task distribution module, a plurality of mobile health task processing modules and a mobile health task callback module; the mobile health task distribution module comprises: a resource description unit, a resource scheduling unit, a resource matcher; each of the mobile health task processing modules comprises: a task decomposition unit, a task execution unit, a task evaluation unit. The present invention is capable of carrying out tasks with full use of the currently available task processing resources, thereby being capable of improving the efficiency and performance of the task scheduling processing in the mobile health environment.

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