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    GENERAL SPEECH ENHANCEMENT METHOD AND APPARATUS USING MULTI-SOURCE AUXILIARY INFORMATION
    1.
    发明公开
    GENERAL SPEECH ENHANCEMENT METHOD AND APPARATUS USING MULTI-SOURCE AUXILIARY INFORMATION 审中-公开

    公开(公告)号：US20240079022A1

    公开(公告)日：2024-03-07

    申请号：US18360838

    申请日：2023-07-28

    Applicant: ZHEJIANG LAB

    Inventor： Jingsong LI ,  Zhenchuan ZHANG ,  Tianshu ZHOU ,  Yu TIAN

    IPC: G10L21/0232 ,  G10L17/02 ,  G10L17/04 ,  G10L25/30

    CPC classification number: G10L21/0232 ,  G10L17/02 ,  G10L17/04 ,  G10L25/30

    Abstract: The present disclosure discloses a general speech enhancement method and apparatus using multi-source auxiliary information. The method includes following steps: S1: building a training data set; S2: using the training data set to learn network parameters of a model, and building a speech enhancement model; S3: building a sound source information database in a pre-collection or on-site collection mode; S4: acquiring an input of the speech enhancement model; and S5: taking a noisy original signal as a main input of the speech enhancement model, taking auxiliary sound signals of a target source group and auxiliary sound signals of an interference source group as side inputs of the speech enhancement model for speech enhancement, and obtaining an enhanced speech signal.

    METHOD AND APPARATUS OF NER-ORIENTED CHINESE CLINICAL TEXT DATA AUGMENTATION
    2.
    发明公开
    METHOD AND APPARATUS OF NER-ORIENTED CHINESE CLINICAL TEXT DATA AUGMENTATION 审中-公开

    公开(公告)号：US20240013000A1

    公开(公告)日：2024-01-11

    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: G06F40/295 ,  G06F40/169 ,  G06F40/30 ,  G06F40/40 ,  G06F40/284

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

    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.

    PATIENT DATA VISUALIZATION METHOD AND SYSTEM FOR ASSISTING DECISION MAKING IN CHRONIC DISEASES
    3.
    发明申请
    PATIENT DATA VISUALIZATION METHOD AND SYSTEM FOR ASSISTING DECISION MAKING IN CHRONIC DISEASES 有权

    公开(公告)号：US20220157468A1

    公开(公告)日：2022-05-19

    申请号：US17553832

    申请日：2021-12-17

    Applicant: ZHEJIANG LAB

    Inventor： Jingsong LI ,  Shiqiang ZHU ,  Tianshu ZHOU ,  Yu TIAN

    IPC: G16H50/50 ,  G16H50/20 ,  G16H50/30 ,  G16H50/70 ,  G16H70/20 ,  G16H10/60

    Abstract: Provided is a patient data visualization method and system for assisting decision making in chronic diseases. According to the present application, a management data model diagram of a patient on a hyperplane is constructed by constructing a chronic disease knowledge graph, and combining static data and dynamic data of the patient, and then the management data model diagram is projected onto a two-dimensional plane. The difference of the Euclidean distance between features of a patient information model on a two-dimensional plane graph from the distance of standard features is compared, and a management plan is generated and recommended in combination with path node concepts and an attribute relationship between the concepts.

    CLINICAL RISK PREDICTION SYSTEM ORIENTED TO DATA DISTRIBUTION DRIFT DETECTION AND SELF-ADAPTATION
    4.
    发明申请
    CLINICAL RISK PREDICTION SYSTEM ORIENTED TO DATA DISTRIBUTION DRIFT DETECTION AND SELF-ADAPTATION 有权

    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.

    METHOD AND SYSTEM FOR AUTOMATICALLY AND QUICKLY DEPLOYING FRONT-END PROCESSOR BASED ON GRAY RELEASE
    5.
    发明公开
    METHOD AND SYSTEM FOR AUTOMATICALLY AND QUICKLY DEPLOYING FRONT-END PROCESSOR BASED ON GRAY RELEASE 审中-公开

    公开(公告)号：US20240036860A1

    公开(公告)日：2024-02-01

    申请号：US18360840

    申请日：2023-07-28

    Applicant: ZHEJIANG LAB

    Inventor： Jingsong LI ,  Hongyi NI ,  Tianshu ZHOU ,  Yu TIAN

    IPC: G06F8/71 ,  G06F8/65

    CPC classification number: G06F8/71 ,  G06F8/65

    Abstract: The present disclosure discloses a method and system for automatically and quickly deploying a front-end processor based on gray release. The system includes a user management module, a front-end processor engineering configuration module, a version iteration module and an engineering code version management repository, where the version iteration module is connected with the engineering code version management repository, the user management module and the front-end processor engineering configuration module, a code is obtained through the engineering code version management repository to perform updating or rollback of a current code, an operating permission of the front-end processor is obtained by using the user management module, an engineering configuration parameter is obtained from the front-end processor engineering configuration module for engineering gray release of a plurality of front-end processors, and a task scheduling function therein is called.

    SYSTEM FOR PREDICTING END-STAGE RENAL DISEASE COMPLICATION RISK BASED ON CONTRASTIVE LEARNING
    6.
    发明公开
    SYSTEM FOR PREDICTING END-STAGE RENAL DISEASE COMPLICATION RISK BASED ON CONTRASTIVE LEARNING 审中-公开

    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.

    MULTI-CENTER SYNERGETIC CANCER PROGNOSIS PREDICTION SYSTEM BASED ON MULTI-SOURCE MIGRATION LEARNING
    7.
    发明申请
    MULTI-CENTER SYNERGETIC CANCER PROGNOSIS PREDICTION SYSTEM BASED ON MULTI-SOURCE MIGRATION LEARNING 有权

    公开(公告)号：US20220093258A1

    公开(公告)日：2022-03-24

    申请号：US17543738

    申请日：2021-12-07

    Applicant: ZHEJIANG LAB

    Inventor： Jingsong LI ,  Yu TIAN ,  Weiguo CHEN ,  Jing MA

    IPC: G16H50/20 ,  G16H50/70 ,  G16H40/20 ,  G16H10/60 ,  A61B5/00

    Abstract: Provided is a multi-center synergetic cancer prognosis prediction system based on multi-source migration learning. The system includes a model parameter setting module, a data screening module, and a multi-source migration learning module, wherein the model parameter setting module is responsible for setting cancer prognosis prediction model parameters; the data screening module is arranged at a clinical center, and a management center transmits the set model parameter to each clinical center, such that each clinical center inquires a sample feature and prognosis index data from a local database according to the model parameter, so as to preprocess the data; and the multi-source migration learning module includes a source model training unit, a migration weight calculation unit, and a target model calculation unit.

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