Abstract: Disclosed is a page multiplexing method, a page multiplexing device, a storage medium and an electronic apparatus. After obtaining the page frame information of pages to be configured in a client to be developed, a component relational tree corresponding to the plurality of pages can be determined. The component relational tree is compared with a pre-constructed reference relational tree to determine a target tree structure composed of target components from the reference relational tree. Dependencies between target components in the reference relational tree match those in the component relational tree. The component code of the target component used by the developed client is queried to multiplex the component code. The component relational tree corresponding to pages to be developed can be compared with the reference relational tree corresponding to each page included in the developed client to determine the component code that can be multiplexed.

Abstract: The present application discloses a label-free adaptive CT super-resolution reconstruction method, device and system based on a generative network, which comprises the following modules: an acquisition module configured for acquiring low-resolution original CT image data; a preprocessing module configured for performing super-resolution reconstruction on original CT images based on total variation to obtain an initial value; and a super-resolution reconstruction module configured for performing high-resolution reconstruction on the initial value. According to the present application, a parameter fine-tuning method is adopted, and a CT reconstruction network which is not suitable for a certain patient is adjusted into a network which is suitable for the patient's situation on the premise of not using a large number of data sets for training; only the low-resolution CT data of the patient is used in this process, and the corresponding high-resolution CT data is not needed as a label.

Abstract: The present application discloses a method and a system for displaying a high-resolution liver cancer pathological image based on an image pyramid, which comprises a data source processing module used for acquiring original images in various states, processing the original images, acquiring an image pyramid, naming image blocks in the image pyramid and storing the image blocks in a folder set for the image pyramid in a server; an image display module used for acquiring the image blocks in the folder set for the image pyramid in the server, acquiring the image blocks according to a user's request and splicing and displaying the image blocks in an image display area, wherein enlargement, reduction and translation operations are supported.

Abstract: The present application discloses a system and a device for functional connectivity matrix processing based on feature selection using a filtering method, which comprises the following steps: acquiring a preprocessed resting state brain functional magnetic resonance image of a subject; extracting time series; calculating a Pearson correlation coefficient to obtain a Pearson correlation coefficient matrix; vectorizing the Pearson correlation coefficient matrix; calculating quantitative correlation indices using a filtering method, and selecting a quantitative correlation index based on a preset threshold; performing weighting processing a selected functional connectivity feature by using the corresponding quantitative correlation index with high correlation with a disease diagnosis result to obtain a functional connectivity matrix; and obtaining a prediction result from the functional connectivity matrix.

Abstract: Disclosed is an image denoising method and apparatus based on wavelet high-frequency channel synthesis. Image data are expanded to a plurality of frequency-domain channels, a plurality of “less-noise” channels and a plurality of “more-noise” channels are grouped through a noise-sort algorithm, and a denoising submodule and a synthesis submodule based on style transfer are combined to form a generative network. A discriminative network is established to add a constraint to the global loss function. After iteratively training the GAN model described above, the denoised image data can be obtained through wavelet inverse transformation. The disclosed algorithm can effectively solve the problem of “blurring” and “loss of details” introduced by traditional filtering or CNN-based deep learning methods, which is especially suitable for noise-overwhelmed image data or high dimensional image data.

Abstract: Disclosed is a method and a system for discovering adverse drug reaction signals based on causal discovery. According to the present application, a causality is introduced in the process of discovering adverse drug reaction signals by using electronic medical record data, the data dimension in real-world electronic medical record data is maximally reserved, a Bayesian network structure containing causal effects, as well as a set of confounding factors which plays a role in both a medication intervention and an occurrence of an adverse event are constructed. The method of constructing the set of confounding factors starts from the data, without artificial access and prior knowledge, and retains the confounding factors in the real world to the greatest extent. A medication intervention group and a control group are constructed based on these confounding factors, and the randomized controlled trial is simulated.

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.

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.

Abstract: The present application discloses a system and a device for functional connectivity matrix processing based on feature selection using a filtering method, which comprises the following steps: acquiring a preprocessed resting state brain functional magnetic resonance image of a subject; extracting time series; calculating a Pearson correlation coefficient to obtain a Pearson correlation coefficient matrix; vectorizing the Pearson correlation coefficient matrix; calculating quantitative correlation indices using a filtering method, and selecting a quantitative correlation index based on a preset threshold; performing weighting processing a selected functional connectivity feature by using the corresponding quantitative correlation index with high correlation with a disease diagnosis result to obtain a functional connectivity matrix; and obtaining a prediction result from the functional connectivity matrix.

Abstract: The present disclosure discloses a medical ETL task dispatching method, system and apparatus based on multiple centers. The method includes following steps: step S1: testing and verifying ETL tasks; step S2: deploying the ETL tasks to a hospital center, and dispatching the ETL tasks to a plurality of executors for execution; step S3: screening an executor set meeting resource demands of ETL tasks to be dispatched; step S4: calculating a current task load of each executor in the executor set; step S5: selecting the executor with a minimum current task load to execute the ETL tasks; and step S6: selecting, by the dispatching machine, the ETL tasks from executor active queues according to a priority for execution. The present disclosure selects the most suitable executor by analyzing a serving index as a task to be dispatched on a current dispatching machine.

Abstract: The present disclosure discloses a multi-component abstract association and fusion method and apparatus in page design. The method includes the following steps: step S1: a construction demand is acquired, and the construction demand is analyzed through a speech recognition method to obtain a natural language text; step S2: an abstract model is constructed by predefining a component library, a rule library and a relationship library, and the abstract model performs components fusion to obtain a JSON structure of a fused component; step S3: the JSON structure of the fused component is escaped into a virtual DOM by using a rendering function, and attributes and events of a virtual DOM node are mapped to obtain a fused component drawing result; and step S4: a real DOM structure is created and interpolated into a real DOM node, so as to realize display of the fused component on a view.

Abstract: Discloses a method and an apparatus for visual construction of a knowledge graph system. In the present disclosure, data permission of a distributed client is determined through a central server. The central server obtains a master template of a knowledge graph system and sends it to the distributed client. The distributed client receives a natural language inputted by a user and parses to generate an abstract syntax tree. The user completes customization of a subtemplate of the knowledge graph system through visual operation. The distributed client encrypts the subtemplate and then sends it to the central server. When the knowledge graph system is to be used, any knowledge concept is inputted, the central server calls and decrypts the subtemplate and then searches a database, and a tree structure knowledge graph is generated and sent to the distributed client.

Abstract: The present disclosure discloses a similar patients identification method and system based on a patient representation image. The method includes following steps: S1: building a healthcare knowledge graph: generating the healthcare knowledge graph by extracting entities and a relationship between the entities in a knowledge source; S2: building a healthcare knowledge graph space vector library; S3: building a patient's personal healthcare knowledge graph space vector data set; S4: drawing a patient's personal healthcare representation image; and S5: performing similar patients identification based on graph similarity calculation. The present disclosure builds a visual patient representation mode, so as to convert patient's healthcare data into a visual image, so that a doctor may intuitively feel a difference of different patients and similarity of similar patients.

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.

Abstract: The present disclosure discloses a publicity-education pushing method and system based on a multi-source information fusion. The method includes: step S1: constructing a patient publicity-education knowledge graph, and pushing the patient publicity-education knowledge graph to a patient through a publicity-education applet; step S2: fusing and correcting patient basic information, patient diagnosis-treatment information, patient eye movement information and a patient personality inventory to obtain patient multi-source information; step S3: constructing a compliance prediction model through a neural network by using the patient multi-source information and collected patient medication taking behavior data; and step S5: building a system rule base, and after searching for a corresponding disease and treatment in the patient publicity-education knowledge graph through information returned by the system rule base, pushing the disease and the treatment to the patient through the publicity-education applet.

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.

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.

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.

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.

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.