公开(公告)号：US20230077818A1

公开(公告)日：2023-03-16

申请号：US17844103

申请日：2022-06-20

Applicant: BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD.

Inventor： Jing HU ,  Guodong ZHAO

IPC: G06N5/02 ,  G16H20/10 ,  G16H70/40 ,  G16H50/70

Abstract: The present disclosure discloses a method for establishing a medicine synergism prediction model, a prediction method and corresponding apparatus, and relates to deep learning and artificial intelligence (AI) medical technologies in the field of AI technologies. A specific implementation solution includes: acquiring a relation graph, nodes in the relation graph including medicine nodes and protein nodes, and edges indicating that interaction exists between the nodes; collecting, from the relation graph, a medicine node pair with definite synergism and a label of whether the medicine node pair has synergism as training samples; and training the medicine synergism prediction model by taking the medicine node pair in the training samples as input to the medicine synergism prediction model and taking the label of whether the medicine node pair has synergism as target output; wherein the medicine synergism prediction model is obtained by learning the relation graph based on a graph attention network.

公开(公告)号：US20230035954A1

公开(公告)日：2023-02-02

申请号：US17844094

申请日：2022-06-20

Applicant: BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD.

Inventor： Jing HU ,  Guodong ZHAO

IPC: G16B45/00 ,  G16B40/20 ,  G16H50/50 ,  G06K9/62 ,  G06F16/901

Abstract: The present disclosure discloses a method for establishing a medicine synergism prediction model, a prediction method and corresponding apparatus, and relates to deep learning and artificial intelligence (AI) medical technologies in the field of AI technologies. A specific implementation solution includes: acquiring a relation graph, nodes in the relation graph including medicine nodes and protein nodes, and edges indicating that interaction exists between the nodes; collecting, from the relation graph, a medicine node pair with definite synergism and a label of whether the medicine node pair has synergism as training samples; and training the medicine synergism prediction model by taking the medicine node pair in the training samples as input to the medicine synergism prediction model and taking the label of whether the medicine node pair has synergism as target output; wherein the medicine synergism prediction model is obtained by learning the relation graph based on a graph convolutional network.