77 results about "Digital medicine" patented technology

The invention relates to the technical field of computers, can be applied to the field of science and technology finance / digital medical treatment, and discloses a method and device for dynamically adjusting threads of a thread pool, electronic equipment and a storage medium. The method comprises the steps that initialization setting is conducted on thread pool parameters through a management center of a target service system; when a thread pool parameter adjustment request is received, generating a first thread pool parameter according to the thread pool parameter adjustment request; receiving a configuration change event through a configuration change monitor, and analyzing the configuration change event to obtain a first thread pool parameter; positioning a thread pool configuration field of the distributed service instance according to a mapping relation between a program entity factory and a parameter cache; injecting the first thread pool parameter into a thread pool component instance of the distributed service instance; and calling the setting function of the distributed service instance, and resetting the corresponding configuration parameters in the thread pool component of the distributed service instance. The method improves the adaptability of the system to load change, and breaks through the system performance bottleneck.

The invention relates to the technical field of automatic testing, can be applied to the field of science and technology finance / digital medical treatment, and discloses an automatic testing method and device for big data, electronic equipment and a storage medium. The method comprises the following steps: acquiring a table structure and field information of distributed storage, and generating a distributed data query statement by using a screened result; generating a scheduling task by using the distributed data query statement, executing the scheduling task, and judging whether data exception exists or not according to an execution result; when a first submission request of a target code is received, performing static analysis on a big data processing script contained in the request, and checking the compliance of grammar and parameters; in the running stage of the target code, parameters of the big data processing script are dynamically analyzed, and transmission logic of the parameters in distributed nodes is tracked; and when a second submission request of the big data scheduling task is received, detecting whether the parameters and the dependency relationship of the big data scheduling task are correct or not. According to the method, the big data platform test efficiency and stability are improved.

The invention relates to the field of artificial intelligence and digital medical treatment, discloses a multi-mode intelligent inquiry method and device, computer equipment and a storage medium, and can be applied to inquiry processing of families, drugstores and medical institutions. The method comprises the following steps: receiving biological characteristic data sent by a user terminal to verify a user identity, and obtaining a historical medical record of a user according to the user identity; receiving chief complaint symptoms and multi-modal physiological data, uploaded by the user terminal, of the user; fusing the chief complaint symptoms, the multi-modal physiological data and historical medical records, and constructing a dynamic knowledge graph; and generating a medication suggestion based on the dynamic knowledge graph and feeding back the medication suggestion to the user terminal. According to the method, the chief complaint symptom and the multi-modal physiological data are collected through the user terminal, intelligent monitoring of the user symptom is achieved, the dynamic knowledge graph is constructed by fusing the chief complaint symptom, the multi-modal physiological data and the historical medical records, efficient diagnosis decision is achieved, and the progress of medical digitization is promoted.

The invention relates to the technical field of digital medical treatment and drug evaluation, in particular to an individualized drug curative effect and safety evaluation method and system based on digital twinborn and causal reinforcement learning. Individualized anti-fact prediction is realized through a patient digital twinning model, and potential curative effects and adverse event risks of different medication schemes can be evaluated on a single patient level. Meanwhile, an off-strategy causal reinforcement learning and dual-robust estimation method is adopted, the individualized value of a long-term sequential medication strategy is evaluated from observed real world data, and the preciseness of causal inference is improved; in addition, a curative effect-safety joint value function is constructed, curative effect benefits, safety risks and compliance cost are uniformly incorporated into the same decision framework, and multi-objective joint optimization is achieved; finally, an interpretability analysis report containing key features, value comparison and risk analysis is provided, the understanding and trust of clinicians on recommendation results are enhanced, and application and popularization in clinical practice are facilitated.

The invention relates to the technical field of medical auxiliary diagnosis, and discloses a hypospadias penis curvature measuring method based on physical simulation inversion. The invention aims to solve the technical pain point that the penile erection curvature and the internal fibrosis focus distribution are difficult to quantify noninvasively and accurately before the operation in the existing hypospadias diagnosis and treatment. According to the method, a standard white mold containing fine anatomical partitions is constructed, personalized geometric mapping is realized by using a static look-around image, and constraint physical parameter inversion is performed in combination with a multi-attitude dynamic stress image, so that the digital twin with real biomechanical properties is reconstructed in a virtual environment. According to the method, the erection bending form can be accurately predicted in the non-erection state, the mechanical property abnormal area range is positioned, operation planning is assisted, experience medical treatment is spanned to accurate digital medical treatment, and the wound diagnosis and treatment and operation risks are remarkably reduced.

The application belongs to the field of digital medicine, and particularly relates to a chronic obstructive pulmonary disease (COPD) differential system based on digital lung sound, a construction method and application thereof. The system comprises a data acquisition module, a preprocessing module, a conversion module, a data module, a data processing module, a data enhancement training module, a deep learning module, a differential module, a grading module and a display module. Digital lung sound records of candidates are collected, converted into mel spectrograms, and diagnosed by using a deep neural network. The candidates are COPD patients, healthy people, or people suffering from other lung diseases, and the disease grade of the COPD patients is given by a device carrying the differential system. The examination only needs to collect lung sounds in clinical or other places, reduces radiation damage and possible infection risk in the conventional diagnosis process, and provides a scheme for early diagnosis of COPD, and improves the diagnosis efficiency of the respiratory department, especially the COPD disease.

The application belongs to the field of artificial intelligence and the field of digital medicine, and relates to a cognitive impairment detection device based on artificial intelligence, a computer device and a storage medium.The device obtains original brain electrical signals of a user, pre-processes the original brain electrical signals to obtain a brain electrical signal sequence, adds a classification mark and position coding to the brain electrical signal sequence to obtain a target brain electrical signal sequence, processes the target brain electrical signal sequence based on an anomaly detection layer in an anomaly detection model to obtain target features, performs prediction processing on the target features based on a full connection layer in the anomaly detection model to obtain a prediction result, generates a numerical comparison result of the prediction result and a preset threshold value, and generates a shallow cognitive impairment detection result corresponding to the user based on the numerical comparison result.In addition, the shallow cognitive impairment detection result can be stored in a blockchain.The application adopts a mode of shallow cognitive impairment detection based on an anomaly detection model, and improves the accuracy of shallow cognitive impairment detection.

The application relates to the fields of artificial intelligence and digital medicine, and proposes a deep learning model self-adaptive testing method based on artificial intelligence and related equipment.The method comprises the following steps: obtaining a deep learning model and test data, the deep learning model can obtain feature representation and classification results of any test data; a first data set for storing the feature representation and the classification results is created; real-time test batches are input into the deep learning model to obtain real-time feature representation and real-time classification results of the test data; the first data set is queried based on the real-time feature representation to construct a feature consistency loss; proxy feature representation of each category in the first data set is calculated to construct a feature uniformity loss; the deep learning model and the first data set are updated based on the feature consistency loss and the feature uniformity loss; and the self-adaptive testing of the deep learning model is completed until a preset condition is met. The application can improve the precision of the deep learning model applied to the field of digital medicine on medical test data.

The application relates to the field of artificial intelligence and digital medicine, and proposes an intention recognition model training method and device based on artificial intelligence, an electronic device and a storage medium. The intention recognition model training method based on artificial intelligence comprises the following steps: performing word embedding on a plurality of natural sentences collected in advance to obtain a sentence vector of each natural sentence; identifying an intention vector and a slot vector of the sentence vector by using a preset semantic coding model; labeling the intention vector and the slot vector to construct a training data set; constructing an initial intention recognition model, training the initial intention recognition model by using the training data set, and obtaining an intention recognition model. The method can jointly train the intention recognition model by using the intention vector and the slot vector of the natural sentence, so that the accuracy of the intention recognition model can be improved.

The application relates to the technical field of artificial intelligence and the field of digital medicine, and provides a model allocation method for edge-cloud cooperative reasoning and an edge-cloud cooperative reasoning method. After model allocation is performed on an edge device and a cloud server, an allocation evaluation result corresponding to the edge device is acquired. For the edge device whose allocation evaluation result does not satisfy an allocation end condition, a new target network model is randomly selected from multiple heterogeneous models for the edge device, a new round of model allocation is performed based on the new target network model, the model allocation scheme is continuously optimized, and the overall reasoning effect of edge-cloud cooperative reasoning is improved.

This application relates to the fields of artificial intelligence and digital medicine, and discloses an infectious disease data processing method, device, computer device, and storage medium, including: obtaining infectious disease data to be processed; preprocessing the infectious disease data according to a preset data dimension to generate a feature matrix; inputting the feature matrix into a preset infectious disease prediction model, and the infectious disease prediction model performs downsampling processing on the feature matrix to generate hidden layer features representing the feature matrix; wherein, the infectious disease prediction model is pre-trained to a convergence state according to an unlabeled sample model for extracting hidden layer information of input data; generating a disease index of the infectious disease data according to the hidden layer features. Writing the disease index into a preset Cartesian coordinate system to generate a schematic diagram of the representation result of the infectious disease data, and inputting the schematic diagram of the representation result into a preset infectiousness recognition model to generate an infectiousness classification result of the infectious disease data.

This application relates to the field of digital medicine, specifically to a method, device, equipment and medium for emotion recognition based on micro-expressions. This method comprehensively considers the two factors that the climax frame in a micro-expression video frame sequence contains more facial information and that the optical flow information can reflect the subtle changes in the face. RGB The two types of climax frames, climax frames and optical flow climax frames, obtain more and more subtle facial features from the micro-expression video to be identified, thereby improving the accuracy of emotion recognition of micro-expressions. At the same time, this method RGB The climax frames and optical flow climax frames are enhanced to increase the number of micro-expression data samples. RGB The number of climax frames and optical flow climax frames increases the number of micro-expression videos in the micro-expression data sample set in disguise. The greatly increased number of samples can improve the accuracy of the training model and further improve the accuracy of emotion recognition of micro-expressions.

The invention provides a construction method of a personalized bone grafting guiding device for femoral neck fracture, which belongs to the technical field of digital medicine, and comprises the following steps: 1, three-dimensional reconstruction, 2, virtual reduction, 3, determination of bone grafting points, 4, determination of a bone attachment surface, using a 3-matic uniform offset tool to uniformly offset the extracted bone attachment surface to the outer side (i.e., the direction opposite to the bone surface) by 1-2 mm, and finally obtaining the personalized bone grafting guiding device for femoral neck fracture. 5, determining a bone grafting path, and 6, generating a kirschner wire sleeve based on the diameter of the kirschner wire so as to generate the bone grafting guider. Effective bony support is provided for the femur by attaching the attaching part to the rear side of the greater trochanter side of the femur of the patient, and the sleeve is guided through the generated kirschner wire. By using the bone grafting guiding device, a Kirschner wire can be accurately guided to a bone defect area, namely a bone grafting target area, in a femoral neck fracture closed reduction operation.

The present application relates to the technical field of digital medicine and drug evaluation, and particularly relates to an individualized drug efficacy and safety evaluation method and system based on digital twinning and causal reinforcement learning. Through a patient digital twinning model, individualized counterfactual prediction is realized, which can evaluate the potential efficacy and adverse event risk of different drug regimens at the individual patient level. Meanwhile, off-policy causal reinforcement learning and double robust estimation methods are used to evaluate the individualized value of long-term sequential drug strategies from observational real-world data, improving the rigor of causal inference. In addition, a combined efficacy-safety value function is constructed, which unifies efficacy benefits, safety risks, and compliance costs into the same decision framework, enabling multi-objective joint optimization. Finally, an interpretable analysis report is provided, including key features, value comparisons, and risk analyses, which enhances clinicians' understanding and trust in the recommended results, facilitating the application in clinical practice.

A method for secure aggregation, by a server, of client-provided inputs includes receiving, from each of a plurality of clients, a respective client input, for which a commitment is published. The commitments were computed using randomness and are aggregated by at least two super-clients and a sum of the aggregated commitments is published by each super-client. A sum of the received client inputs is published such that validity of the sum is checkable, by the clients, by comparing the sum of the received client inputs to a verification algorithm result that uses a sum of additive shares computed by the clients using the randomness, and by verifying that the published sum of the aggregated commitments is the same for each super-client. The method can be applied to use cases, for example, in digital medicine using medical data or smartcity applications to support decision-making.

The embodiment of the invention provides a test case generation method and device, electronic equipment and a storage medium, belongs to the technical field of computers, and is suitable for the field of financial science and technology and the field of digital medical treatment. The method comprises the steps of obtaining object operation behaviors, clustering the object operation behaviors to obtain an operation behavior cluster which is provided with a target cluster center, performing sequence prediction on the target cluster center through a sequence generation model to obtain an operation behavior sequence, screening out target operation from a preset action space according to the operation behavior sequence, and executing the target operation on the operation behavior sequence to obtain the target behavior sequence, and generating the target test case according to the target behavior sequence, so that the software test efficiency can be improved.

The application discloses a medical intention recognition method and device, a storage medium and computer equipment, relates to the technical field of digital medicine, and mainly aims to solve the problems of poor recognition accuracy and effectiveness of the existing medical intention. The method comprises the following steps: obtaining dialogue text to be recognized; performing recognition on the dialogue text based on an intention recognition model that has completed model training, to obtain a first medical intention, wherein the intention recognition model is trained based on a loss function constructed based on a positive sample pair and a negative sample pair, and text training samples; the positive sample pair and the negative sample pair are determined based on opposite statements in the text training samples; determining a second medical intention matched with the first medical intention based on a generated medical knowledge graph, and generating a medical intention recognition result based on the first medical intention and the second medical intention.

The invention discloses an enzymatic colorimetric coding-decoding system for weighted detection of multiple biomarkers and application, and belongs to the field of biomedical detection. According to the enzymatic colorimetric coding-decoding system provided by the invention, the mapping relation between the color and the multi-dimensional biomarker can be realized through direct visual interpretation or simple spectral analysis. The invention also provides an enzymatic colorimetric coding-decoding method for weighted detection of multiple biomarkers, and the method comprises the following steps: converting multi-dimensional biomarker information into quantifiable and visually analyzable optical signals through an integrase catalyzed multicolor reaction, nucleic acid driven signal amplification and optical coding-decoding mechanism; and single-tube multi-dimensional joint detection, high information density output and rapid disease state discrimination are realized. The method is successfully applied to early screening of pancreatic cancer and can be expanded to dynamic monitoring of multiple markers of other cancers and infectious diseases in the future, and an efficient and low-cost solution is provided for precise medical treatment and digital medicine.

The utility model discloses an intelligent digital medicine basket cleaning machine relates to medicine basket cleaning technical field, specifically includes feeding chamber, cleaning and drying chamber and material stacking car, the top of feeding chamber is provided with first medicine basket conveying structure, one end of first medicine basket conveying structure extends to the outside of feeding chamber, the top of another end of first medicine basket conveying structure is provided with the material baffle, the side away from cleaning and drying chamber of material baffle is provided with the material pushing board, the material pushing board is connected with feeding chamber through electric telescopic link no. The second medicine basket conveying structure is arranged in the cleaning and drying chamber. Adopt the mode of spray cleaning, spray flushing, realize the cleaning of medicine basket, compared with the immersion ultrasonic cleaning, can reduce water resource consumption, and can avoid medicine basket to receive secondary pollution in the cleaning process, and in the cleaning and drying process, medicine basket position can change, can realize all -round cleaning and drying, guarantee cleaning effect.

The invention discloses an atrium modeling method based on nnUnet, and belongs to the field of digital medical treatment. The method comprises the following steps: firstly, obtaining a trained nnUnnet-v2 deep learning network, and obtaining adventitia areas of a left atrium and a right atrium of atrial LGE-MRI image data to be subjected to atrial modeling based on the trained nnUnnet-v2 deep learning network; then myocardial areas of the left atrium and the right atrium and blood pool areas of the left atrium and the right atrium are obtained, the strength of all myocardial signal mark points of the left atrium and the right atrium and the strength of all blood pool signal mark points of the left atrium and the right atrium are obtained, and finally fibrosis areas of the left atrium and the right atrium are obtained and subjected to fibrosis labeling. Then linear interpolation is carried out on the basis of the myocardial areas of the left atrium and the right atrium subjected to fibrosis labeling, and a primary atrium three-dimensional model is obtained; and repairing and optimizing the primary atrium three-dimensional model to obtain the atrium three-dimensional model. The method can be widely applied to the fields of atrial disease research, treatment planning and related medical image analysis.

The embodiment of the invention provides a multi-thread-based text reading method and device, electronic equipment and a storage medium, belongs to the technical field of computers, and is applied to the field of financial science and technology and the field of digital medical treatment. The method comprises the steps of obtaining a text capacity and a text reading mode of a target text, segmenting the target text into reference text blocks according to the text capacity, obtaining metadata of the reference text blocks, packaging the metadata into a text reading task, distributing the text reading task to at least two target threads, and for each target thread, obtaining a text reading mode of the target text; the text reading position is obtained according to the text reading task and the text reading mode, the reference text blocks are screened according to the text reading position, the read text blocks are obtained, the read text blocks are read through the target thread, and the text reading efficiency can be improved.

The application relates to the field of intelligent decision-making and the field of digital medicine, and discloses a video retrieval method and device based on multiple modes, electronic equipment and a storage medium, the method comprising the following steps: querying multiple mode attributes of a to-be-retrieved video, selecting a multiple mode network, and extracting multiple mode features in the to-be-retrieved video; extracting a feature sequence, constructing a fixed cluster group of the feature sequence, performing mean pooling on the fixed cluster group to obtain a pooled cluster group, performing fixed-length alignment on the feature sequence according to the pooled cluster group to obtain an aligned sequence; extracting a feature dimension, aligning the dimension length to obtain an aligned dimension, and determining aligned multiple mode features of the multiple mode features according to the aligned sequence and the aligned dimension; performing feature splicing and feature fusion; performing feature vector coding on a retrieval text, calculating a cosine similarity, and determining a video retrieval result of the retrieval text according to the cosine similarity. The application can realize automatic indexing and retrieval of videos and improve the comprehensiveness of the entire video retrieval process.

The present invention relates to an intelligent recommendation device for clinical informed consent based on the BERT model, belonging to the field of digital medicine. It comprises: a data acquisition and processing module for collecting and preprocessing patients' historical medical information, medical knowledge, and clinical informed consent samples; a model construction module for obtaining a trained clinical informed consent generation model; a knowledge base construction module for extracting key information and terms through the trained clinical informed consent generation model and constructing a clinical informed consent template library and a term list library; a clinical informed consent generation module for inputting the data of the patient to be tested into the trained clinical informed consent generation model and calling the clinical informed consent template and term list to generate a personalized clinical informed consent; and a doctor-patient interaction platform for providing a chat window. The device automatically generates personalized clinical informed consent forms for patients, improving work efficiency while enabling patients to understand the content more accurately.

Embodiments of the present application disclose an image segmentation method, device and equipment and a storage medium, which can be applied to the field of digital medicine. The method comprises: obtaining a target image to be segmented; determining a local image block in the target image and a global image block matching the size of the local image block; inputting the local image block and the global image block into a preset segmentation network model for image segmentation processing, wherein the preset segmentation network model is configured to: perform feature fusion on the input local image block and global image block, and perform segmentation processing based on the fused target feature information; and determining a segmentation result corresponding to the target image according to the output of the preset segmentation network model. The technical solution of the embodiments of the present application can ensure the accuracy of image segmentation.

The application relates to the fields of computer technology and digital medicine, and discloses an intelligent processing method and system of an SQL statement, a computer device and a medium. The method comprises the following steps: when a data operation request for a database is detected, the data operation request is intercepted and analyzed to generate database connection information; when it is determined that a target SQL script is a preset first type SQL, a target execution plan of the target SQL script is determined according to the database connection information, and a to-be-optimized parameter of the target SQL script is determined based on the plan to optimize the target SQL script. Through real-time interception of the data operation request for preset first type SQL judgment, when it is determined that the target SQL script corresponding to the request is the preset first type SQL, a target execution plan at the current time can be created to determine the to-be-optimized parameter for SQL optimization, so that each data request corresponds to the current latest execution plan, accurate reproduction and optimization of the preset first type SQL can be realized, and the stability of the system is improved.

The invention discloses a page loading method and device, a medium and equipment, and relates to the field of financial science and technology and digital medical treatment, and the method comprises the steps: obtaining first resource positioning information corresponding to a current page and second resource positioning information corresponding to a to-be-loaded page, judging whether the first resource positioning information and the second resource positioning information are the same resource positioning information or not; when the first resource positioning information and the second resource positioning information are the same resource positioning information, acquiring a first total content fragment corresponding to the first resource positioning information and a second total content fragment corresponding to the second resource positioning information, and comparing the first total content fragment with the second total content fragment; determining a processing mode of the resource positioning information according to a comparison result; in the embodiment of the invention, the second resource positioning information is modified according to the processing mode of the resource positioning information, and the page loading is performed according to the modified second resource positioning information, so that the problem that the content of the page loaded by the WebView component is not refreshed due to the fact that the resource positioning information is not refreshed is avoided.

The invention relates to the technical field of big data, can be applied to the field of science and technology finance / digital medical treatment, and discloses an automatic offline method and device for a big database table, electronic equipment and a storage medium. The method comprises the following steps: setting a permission approval person of a virtual user in a target distributed computing framework as an owner of the virtual user, and synchronizing owner information and permission configuration change information; automatically synchronizing dependency, authorization records and auditing records at the downstream of the library table to a data middle table; performing automatic cleaning and conversion on downstream dependency, authorization records and auditing records of the library table; generating a unified wide table according to downstream dependency, authorization records and auditing records of the library table; receiving an input library table to be evaluated offline, generating evaluation information and virtual user owner information of the library table to be evaluated offline, and sending an offline notification to an associated party for confirmation; and receiving feedback confirmation information returned by the related party, and performing offline processing on the to-be-offline evaluation library table. According to the method, the automation degree and efficiency of large database table offline are improved.

The present invention discloses a privacy protection method and system based on homomorphic authentication encryption, which belongs to the field of digital medicine. The method includes initializing the system and the medical cloud server; the medical data center encrypts the trained diagnostic model and uploads it to the medical cloud server; after the system user obtains authorization, the personal privacy data collected by the wearable device is extracted to form its query vector and then sent to the medical cloud server. The medical cloud server calculates in a ciphertext state and sends the result back to the user. The user decrypts to obtain the final result and completes the online medical service. The homomorphic authentication encryption algorithm proposed in the present invention uses this algorithm to achieve bilateral privacy protection and authenticity. By using this algorithm, the privacy and integrity of the user privacy, the medical data center data and the diagnostic results are ensured at the same time. And because the algorithm is simple, it can be implemented in many lightweight terminals, which is more user-friendly.

The embodiments of the present application belong to the fields of image processing technology and digital medicine, and relate to a model training method and related equipment for asymmetric image retrieval. The present application uses a feature space mapping model instead of distillation learning to achieve asymmetric retrieval. Because the features of the large model and the small model are remapped to a new feature space, it can break through the upper limit of the accuracy of distillation learning and obtain higher asymmetric retrieval accuracy. At the same time, when the large model needs to be updated after the amount of data accumulates for a period of time, distillation learning needs to retrain all models. However, since the training of the small model in this method is relatively independent of the large model, only the large model and its mapping model can be trained and updated for parameters, saving the resources required to update the model.

The invention relates to the technical field of crossing of orthodontics and digital medical treatment, and provides an orthodontic target position generation method based on cranio-maxillofacial skeleton harmony, which comprises the following steps: S1, acquiring multi-modal oral data of a patient, performing registration fusion, and constructing a three-dimensional integrated virtual model containing jaw bones and teeth; s2, performing bone form quantitative analysis, and automatically identifying the bone type and main bony limitation characteristics of the patient; s3, on the basis of the skeleton constraint conditions, solving and generating an individualized skeleton coordination correction target position matched with the skeleton form of the patient through a preset coordination degree evaluation function and a multi-objective optimization algorithm; s4, presenting the individualized bone coordination correction target position and an analysis basis through a visual interaction interface; and S5, by taking the confirmed individualized bone coordination correction target position as a reference, finishing tooth movement sequence planning of invisible correction and correction related component design. The defect of excessive or improper compensation caused by neglecting the skeleton basis in the prior art is overcome.