公开(公告)号：US20240145059A1

公开(公告)日：2024-05-02

申请号：US18364470

申请日：2023-08-02

Applicant: ZHEJIANG LAB

Inventor： Jingsong LI ,  Yu WANG ,  Shuang MA ,  Yu TIAN ,  Tianshu ZHOU

IPC: G16H20/10 ,  G16H10/20 ,  G16H10/60

CPC classification number: G16H20/10 ,  G16H10/20 ,  G16H10/60

Abstract: Disclosed is a method and a system for discovering adverse drug reaction signals based on causal discovery. According to the present application, a causality is introduced in the process of discovering adverse drug reaction signals by using electronic medical record data, the data dimension in real-world electronic medical record data is maximally reserved, a Bayesian network structure containing causal effects, as well as a set of confounding factors which plays a role in both a medication intervention and an occurrence of an adverse event are constructed. The method of constructing the set of confounding factors starts from the data, without artificial access and prior knowledge, and retains the confounding factors in the real world to the greatest extent. A medication intervention group and a control group are constructed based on these confounding factors, and the randomized controlled trial is simulated.

公开(公告)号：US20240078678A1

公开(公告)日：2024-03-07

申请号：US18360796

申请日：2023-07-27

Applicant: ZHEJIANG LAB

Inventor： Jingsong LI ,  Jun LI ,  Baochen WANG ,  Zhuoxin LI ,  Yu TIAN ,  Tianshu ZHOU

IPC: G06T7/00

CPC classification number: G06T7/0014 ,  G06T2207/10088 ,  G06T2207/30016

Abstract: The present application discloses a system and a device for functional connectivity matrix processing based on feature selection using a filtering method, which comprises the following steps: acquiring a preprocessed resting state brain functional magnetic resonance image of a subject; extracting time series; calculating a Pearson correlation coefficient to obtain a Pearson correlation coefficient matrix; vectorizing the Pearson correlation coefficient matrix; calculating quantitative correlation indices using a filtering method, and selecting a quantitative correlation index based on a preset threshold; performing weighting processing a selected functional connectivity feature by using the corresponding quantitative correlation index with high correlation with a disease diagnosis result to obtain a functional connectivity matrix; and obtaining a prediction result from the functional connectivity matrix.

公开(公告)号：US20240071607A1

公开(公告)日：2024-02-29

申请号：US18363701

申请日：2023-08-01

Applicant: ZHEJIANG LAB

Inventor： Jingsong LI ,  Wenchao XIANG ,  Guangyuan DENG ,  Tianshu ZHOU ,  Yu TIAN

IPC: G16H40/20 ,  G06F9/48 ,  G06F16/25 ,  G16H10/60

CPC classification number: G16H40/20 ,  G06F9/4881 ,  G06F16/254 ,  G16H10/60

Abstract: The present disclosure discloses a medical ETL task dispatching method, system and apparatus based on multiple centers. The method includes following steps: step S1: testing and verifying ETL tasks; step S2: deploying the ETL tasks to a hospital center, and dispatching the ETL tasks to a plurality of executors for execution; step S3: screening an executor set meeting resource demands of ETL tasks to be dispatched; step S4: calculating a current task load of each executor in the executor set; step S5: selecting the executor with a minimum current task load to execute the ETL tasks; and step S6: selecting, by the dispatching machine, the ETL tasks from executor active queues according to a priority for execution. The present disclosure selects the most suitable executor by analyzing a serving index as a task to be dispatched on a current dispatching machine.

公开(公告)号：US20230409728A1

公开(公告)日：2023-12-21

申请号：US18336053

申请日：2023-06-16

Applicant: ZHEJIANG LAB

Inventor： Jingsong LI ,  Guangyuan DENG ,  Tianshu ZHOU ,  Yu TIAN

IPC: G06F21/62 ,  G06F16/901 ,  G06F21/60 ,  H04L9/30

CPC classification number: G06F21/6218 ,  G06F16/9024 ,  G06F21/602 ,  H04L9/3006

Abstract: Discloses a method and an apparatus for visual construction of a knowledge graph system. In the present disclosure, data permission of a distributed client is determined through a central server. The central server obtains a master template of a knowledge graph system and sends it to the distributed client. The distributed client receives a natural language inputted by a user and parses to generate an abstract syntax tree. The user completes customization of a subtemplate of the knowledge graph system through visual operation. The distributed client encrypts the subtemplate and then sends it to the central server. When the knowledge graph system is to be used, any knowledge concept is inputted, the central server calls and decrypts the subtemplate and then searches a database, and a tree structure knowledge graph is generated and sent to the distributed client.

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.