公开(公告)号：US11568996B2

公开(公告)日：2023-01-31

申请号：US17541301

申请日：2021-12-03

Applicant: ZHEJIANG LAB

Inventor： Jingsong Li ,  Yu Tian ,  Yong Shang ,  Ran Xin

IPC: G16H10/60 ,  G16H50/50 ,  G16H50/20

Abstract: Provided is a cross-departmental decision support system for early diagnosis of a chronic kidney disease based on knowledge graph, which comprises a patient information model building module, a patient information model library storage module, a knowledge graph association module, a knowledge graph inference module and a decision support feedback module. According to the present application, by constructing a patient information model and utilizing an OMOP CDM standard terminology system, patient electronic medical record data is constructed into a patient information model with unified concept coding and unified semantic structure; making full use the advantages of semantic technology in data interactivity and scalability, so that the system has better adaptability and scalability to heterogeneous data in different hospitals.

公开(公告)号：US11972214B2

公开(公告)日：2024-04-30

申请号：US18348317

申请日：2023-07-06

Applicant: ZHEJIANG LAB

Inventor： Jingsong Li ,  Lixin Shi ,  Ran Xin ,  Zongfeng Yang ,  Yu Tian ,  Tianshu Zhou

IPC: G06F17/00 ,  G06F40/169 ,  G06F40/284 ,  G06F40/295 ,  G06F40/30 ,  G06F40/40

CPC classification number: G06F40/295 ,  G06F40/169 ,  G06F40/284 ,  G06F40/30 ,  G06F40/40

Abstract: Disclosed is a method and an apparatus NER-orientated Chinese clinical text data augmentation, and unannotated data and annotated data of label linearization processing through data preprocessing. A concealed part is predicted based on retained information by using the unannotated data and concealing part of information in text, and meanwhile an entity word-level discrimination task is introduced for pre-training of a span-based language model; and a plurality of decoding mechanisms are introduced in a fine-tune stage, a relationship between a text vector and text data is obtained based on the pre-trained span-based language model, linearized data with entity labels is converted into the text vector, and text generation is performed through forward decoding and reverse decoding in a prediction stage of a text generation model to obtain enhanced data with annotation information.