公开(公告)号：US20240212862A1

公开(公告)日：2024-06-27

申请号：US18595379

申请日：2024-03-04

Applicant: ZHEJIANG LAB

Inventor： Jingsong LI ,  Feng WANG ,  Hang ZHANG ,  Shengqiang CHI ,  Yu TIAN ,  Tianshu ZHOU

IPC: G16H50/50 ,  G06N3/0475 ,  G06N5/022

CPC classification number: G16H50/50 ,  G06N3/0475 ,  G06N5/022

Abstract: Disclosed is a general multi-disease prediction system based on causal check data generation. For a general scenario, the present invention provides a tendency score calculation method based on a general tendency score network from the perspective of causality; compared with the problem of poor interpretability of traditional generative adversarial networks, the present invention provides a generative adversarial network based on causal check, so that generated data better conforms to real causal logic; in view of the problem that existing graph convolutional neural networks are modeled only from the perspective of correlation, the present invention provides a general multi-disease prediction model based on a general causal graph convolutional neural network, and a causal effect value is integrated to improve the prediction performance of the general multi-disease prediction system on diseases, thereby solving the problems of poor model performance and low robustness caused by few training samples in a general scenario.

公开(公告)号：US20220092430A1

公开(公告)日：2022-03-24

申请号：US17541298

申请日：2021-12-03

Applicant: ZHEJIANG LAB

Inventor： Jingsong LI ,  Tianshu ZHOU ,  Ziyue YANG ,  Shengqiang CHI

IPC: G06N3/08 ,  G06N3/04 ,  G06K9/00 ,  G06K9/62

Abstract: Provided is a time series deep survival analysis system combined with active learning. The system includes: a data collection module, an active learning module, and a time series deep survival analysis module; the data collection module is used for obtaining survival data of objects to be analyzed; combined with an active learning method, the active learning module selects a part of right censored data to label a survival time; and the time series deep survival analysis module constructs a time series deep survival analysis neural network model, and takes uncensored data and right censored data as model inputs, so as to obtain survival time prediction results of the objects to be analyzed. The present application can make full use of the right censored data in the survival data and time series features.

公开(公告)号：US20250014754A1

公开(公告)日：2025-01-09

申请号：US18635048

申请日：2024-04-15

Applicant: ZHEJIANG LAB

Inventor： Jingsong LI ,  Shengqiang CHI ,  Feng WANG ,  Tianshu ZHOU ,  Yu TIAN

IPC: G16H50/30 ,  G06F18/2413 ,  G16H50/20

Abstract: A clinical risk prediction system oriented to data distribution drift detection and self-adaptation, comprising a central server comprising a first drift detection module and a model aggregation module, and nodes comprising a data acquisition module configured to acquire patient clinical diagnosis and treatment data, a second drift detection module and a model updating module. The first and second drift detection module determine whether the patient clinical diagnosis and treatment data distribution has drifted according to whether the new/old patient clinical diagnosis and treatment data set comes from the same data distribution. When the data distribution has drifted, a local clinical risk prediction model is trained, and its parameters are uploaded to the central server and aggregated to obtain an updated model, which is issued to each node for deployment. The new patient clinical diagnosis and treatment data is input into the updated model to obtain a clinical risk prediction result.

公开(公告)号：US20240021312A1

公开(公告)日：2024-01-18

申请号：US18352216

申请日：2023-07-13

Applicant: ZHEJIANG LAB

Inventor： Jingsong LI ,  Feng WANG ,  Shengqiang CHI ,  Yu TIAN ,  Tianshu ZHOU

IPC: G16H50/20 ,  G16H50/30

CPC classification number: G16H50/20 ,  G16H50/30

Abstract: Disclosed is an system for predicting end-stage renal disease complication risk based on contrastive learning, including an end-stage renal disease data preparation module, configured to extract structured data of a patient by using a hospital electronic information system and daily monitoring equipment, and process the structured data to obtain augmented structured data; and a complication risk prediction module, configured to construct a complication representation learning model and a complication risk prediction model, perform training and learning on the augmented structured data through the complication representation learning model to obtain a complication representation, and perform end-stage renal disease complication risk prediction by using the complication representation through the complication risk prediction model.