公开(公告)号：US20240047051A1

公开(公告)日：2024-02-08

申请号：US18353865

申请日：2023-07-17

Applicant: ZHEJIANG LAB

Inventor： Jingsong LI ,  Jing XIA ,  Yinghao ZHAO ,  Yu TIAN ,  Tianshu ZHOU

IPC: G16H40/20 ,  G16H50/20 ,  B64U10/00

CPC classification number: G16H40/20 ,  G16H50/20 ,  B64U10/00 ,  G06Q50/28

Abstract: Disclosed is an emergency blood dispatching method and system based on early prediction and unmanned fast delivery. In the present disclosure, an emergency blood use prediction model and an unmanned aerial vehicle fast delivery route are introduced, blood use demands of pre-hospital emergency trauma patients are accurately predicted, pre-hospital emergency blood transfusion of patients is achieved through unmanned aerial vehicle sites, it does not need to consume a lot of road traffic time to arrive at a hospital and then starts blood transfusion, the speed of blood supply and treatment quality of the patients with massive traumatic hemorrhage are improved, and it is of great value to rescue remote mountain trauma patients. The present disclosure evaluates blood use demands of the hospital in real time, and combines an unmanned aerial vehicle and a blood delivery car to fast deliver needed blood products from a blood center to the hospital.

公开(公告)号：US20240029846A1

公开(公告)日：2024-01-25

申请号：US18362950

申请日：2023-07-31

Applicant: ZHEJIANG LAB

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

IPC: G16H10/60 ,  G16C20/30

CPC classification number: G16H10/60 ,  G16C20/30

Abstract: Disclosed is a method and a system for discovering new drug indications by fusing patient portrait information. According to the present disclosure, real-world patient medication and patient diagnostic data are introduced into a data-driven drug relocation solution, an actual use effect of drugs in a broader population is added into a new drug-disease relationship prediction model. According to the present disclosure, a patient portrait is constructed as a feature expression of patient information, and is used to construct a patient-patient network as a medium between drug and disease networks, and a heterogeneous network system corresponding to actual clinical processes is constructed. Prediction results in the present disclosure are more closely related to a clinical practice, and a probability of success in subsequent validation of old drugs for new usage and new clinical trials is greater.

公开(公告)号：US20220093268A1

公开(公告)日：2022-03-24

申请号：US17541301

申请日：2021-12-03

Applicant: ZHEJIANG LAB

Inventor： Jingsong LI ,  Yu TIAN ,  Yong SHANG ,  Ran XIN

IPC: G16H50/50 ,  G16H10/60 ,  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：US20220092789A1

公开(公告)日：2022-03-24

申请号：US17541271

申请日：2021-12-03

Applicant: ZHEJIANG LAB

Inventor： Jingsong LI ,  Peijun HU ,  Yu TIAN ,  Tianshu ZHOU

IPC: G06T7/11 ,  G06N3/08

Abstract: The present invention discloses an automatic pancreas CT segmentation method based on a saliency-aware densely connected dilated convolutional neural network. Under a coarse-to-fine two-step segmentation framework, the method uses a densely connected dilated convolutional neural network as a basis network architecture to obtain multi-scale image feature expression of the target. An initial segmentation probability map of the pancreas is predicted in the coarse segmentation stage. A saliency map is then calculated through saliency transformation based on a geodesic distance transformation. A saliency-aware module is introduced into the feature extraction layer of the densely connected dilated convolutional neural network, and the saliency-aware densely connected dilated convolutional neural network is constructed as the fine segmentation network model. A coarse segmentation model and the fine segmentation model are trained using a training set, respectively.