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.