公开(公告)号：US20240054360A1

公开(公告)日：2024-02-15

申请号：US18358051

申请日：2023-07-25

Applicant: ZHEJIANG LAB

Inventor： Tianshu ZHOU ,  Yifan JIANG ,  Jingsong LI ,  Yu TIAN ,  Ying ZHANG

IPC: G06N5/022 ,  G06T5/10 ,  G06T11/20 ,  G06V10/75 ,  G06V10/74 ,  G16H10/60 ,  G16H50/70

CPC classification number: G06N5/022 ,  G06T5/10 ,  G06T11/20 ,  G06V10/751 ,  G06V10/761 ,  G16H10/60 ,  G16H50/70 ,  G06T2207/20052

Abstract: The present disclosure discloses a similar patients identification method and system based on a patient representation image. The method includes following steps: S1: building a healthcare knowledge graph: generating the healthcare knowledge graph by extracting entities and a relationship between the entities in a knowledge source; S2: building a healthcare knowledge graph space vector library; S3: building a patient's personal healthcare knowledge graph space vector data set; S4: drawing a patient's personal healthcare representation image; and S5: performing similar patients identification based on graph similarity calculation. The present disclosure builds a visual patient representation mode, so as to convert patient's healthcare data into a visual image, so that a doctor may intuitively feel a difference of different patients and similarity of similar patients.

公开(公告)号：US20240038083A1

公开(公告)日：2024-02-01

申请号：US18360832

申请日：2023-07-28

Applicant: ZHEJIANG LAB

Inventor： Jingsong LI ,  Huiyao SUN ,  Tianshu ZHOU ,  Yu TIAN ,  Ying ZHANG

IPC: G09B5/02 ,  G16H50/20 ,  G16H20/10 ,  G06N20/00

CPC classification number: G09B5/02 ,  G16H50/20 ,  G16H20/10 ,  G06N20/00 ,  G16H10/60

Abstract: The present disclosure discloses a publicity-education pushing method and system based on a multi-source information fusion. The method includes: step S1: constructing a patient publicity-education knowledge graph, and pushing the patient publicity-education knowledge graph to a patient through a publicity-education applet; step S2: fusing and correcting patient basic information, patient diagnosis-treatment information, patient eye movement information and a patient personality inventory to obtain patient multi-source information; step S3: constructing a compliance prediction model through a neural network by using the patient multi-source information and collected patient medication taking behavior data; and step S5: building a system rule base, and after searching for a corresponding disease and treatment in the patient publicity-education knowledge graph through information returned by the system rule base, pushing the disease and the treatment to the patient through the publicity-education applet.

公开(公告)号：US20220093257A1

公开(公告)日：2022-03-24

申请号：US17543736

申请日：2021-12-07

Applicant: ZHEJIANG LAB

Inventor： Jingsong LI ,  Tianshu ZHOU ,  Chengkai WU ,  Ying ZHANG

IPC: G16H50/20 ,  G16H10/60

Abstract: Provided is a system for the prognostics of the chronic diseases after the medical examination based on the multi-label learning, including a data acquisition module, a data preprocessing module, a basic predicting model constructing module, and a local predicting module. The data acquisition module is configured to acquire physical examination data of a physical examination user. The basic predicting model constructing module is configured to construct a multi-label learning model for a physical examination scenario. The local predicting module includes a local model training unit and a predicting unit. The local model training unit adjusts the basic predicting model into a local predicting model, and solidifies the local predicting model into the local predicting module. The predicting unit outputs a predicted prognostic index for an occurrence of a plurality of chronic diseases, and finally acquires a future expected occurrence time of the chronic diseases.