55 results about "Diagnosis tool" patented technology

The invention relates to the field of disease prediction, in particular to an epilepsy prediction system based on synchronously acquired electroencephalogram-gastric electrical signals, comprising: a data acquisition module for acquiring data including electroencephalogram data and gastrointestinal electrical data; the first analysis module is used for obtaining an average electrode inconsistency index of the first channel and the second channel based on a first model according to the time sequence data; and the prediction module is used for comparing the average electrode inconsistency index of the subject with a preset evaluation threshold value so as to predict an epileptic. The system provided by the invention can more accurately and efficiently distinguish epilepsy patients from healthy people, and can be used as an auxiliary diagnosis tool to be applied to primary screening of epilepsy of relatively large people (such as communities and physical examination centers).

The invention relates to the technical field of medical and multi-modal large models, and discloses a disease prediction and auxiliary diagnosis system construction method and system based on a multi-modal large model. The method comprises the steps of report format conversion, data cleaning and table image-to-structured text conversion. Constructing a retrieval knowledge base to enhance the retrieval capability; precise cue word design and reasoning optimization are carried out; small sample learning and model fine tuning; the invention discloses a multi-modal large model integration and visualization system. According to the system, the accuracy problem of a traditional disease risk prediction method is effectively solved, and a more reliable auxiliary diagnosis tool is provided.

Methods and systems are provided for information retrieval pathway guidance. In particular, the systems and methods provided apply artificial intelligence to novel tasks related to retrieving remotely accessible data such as detecting competency levels of users, generating personalized guidance for individual users based on the retrieval goals and initial competency level of a user, generating custom diagnostic assets for those goals based on current strengths and weaknesses, generating content for custom questions for those diagnostic assets, and dynamically tracking and updating the competency level of the user during the course.

The invention provides a breast cancer patient pCR prediction classification method based on image-gene interpretability deep learning. The method comprises the following steps: dividing an obtained data set into a training set and a test set; splitting all the three-dimensional MRI images into two-dimensional slice images; the gene text features of each breast cancer patient are classified into N different sets; segmenting a two-dimensional slice image corresponding to the training set into a plurality of patches; each patch and the gene text feature corresponding to each patch are converted into a corresponding high-dimensional Embedding (high-dimensional Embedding); respectively multiplying H by Wk and Wv to obtain K and V; multiplying G by WQ to obtain Q; obtaining a gene guidance image feature CoAttn; the CoAttn and the Q are fused to obtain a feature set F; inputting the F into a network model, and training the network model based on a loss function; inputting the two-dimensional slice image corresponding to the test set into the trained network model, and outputting a plurality of classification results; and adopting a maximum voting strategy to make decisions on the plurality of classification results. According to the method, pCR and npCR classification is carried out on the patient based on a co-attention mechanism of gene text features and MRI images, the interpretability of model decision is enhanced, and a more visual and reliable auxiliary diagnosis tool is provided for clinicians.

The invention discloses a bidirectional reasoning diagnosis method based on a large model, which can be applied to a clinical intelligent diagnosis scene. Firstly, key abnormal features in hospital admission records of patients are concerned through abnormal feature extraction; secondly, similar medical records are retrieved as diagnosis experience learning; thirdly, forward reasoning is deduced from the features of the patient, and possible diseases are deduced; in the reverse reasoning, a cause tracing method is adopted, and related characteristics are traced back from a diagnosis result. And through combination of deduction and induction, the reasoning ability of the large model is enhanced. And finally, carrying out correction and confidence evaluation on the diagnosis result to realize iterative reflection optimization and ensure the reliability and interpretability of the diagnosis process. An automatic diagnosis tool based on a large model is provided for the medical field, the diagnosis accuracy and efficiency can be improved, and an innovative solution is provided for clinical intelligent diagnosis.

The invention provides a multi-modal sarcopenia diagnosis system, method and device and a medium, a technical framework of the multi-modal sarcopenia diagnosis system, method and device integrates multi-modal data integration, a complex neural network technology, a personalized meta-learning algorithm and an interpretable artificial intelligence technology, and aims to create an accurate sarcopenia diagnosis tool with high adaptability. The method not only makes a breakthrough on the basis of a traditional bioelectrical impedance technology, but also shows a huge potential of realizing efficient and personalized diagnosis and monitoring in diversified medical environments through application of multi-modal data fusion and an intelligent model.

The invention relates to a method for constructing a radio and television service fault diagnosis agent, which comprises the following steps of: 1, completing the construction of a basic agent project based on an MCP architecture, realizing the construction, deployment and association of three components, namely Host, Client and Server, of the MCP, and calling and packaging an existing diagnosis service API; step 2, realizing Qamp; the retrieval of the A document is enhanced and optimized, so that the response accuracy of an agent component MCP Host is improved; 3, based on the semantic slicing technology, retrieval enhancement optimization of a service fault diagnosis guide manual is achieved, and the decision accuracy of an agent component MCP Host is improved; 4, management, association, prompt word optimization and vectorization of an API document are achieved based on the semantic slicing technology, the description detail degree of a tool under an agent component MCP Server on an MCP Host is improved, and the hit rate and the accuracy rate of the MCP Server diagnosis tool are called; the problem that the manual service process of the current traditional radio and television service fault diagnosis scene is low in efficiency is solved, and the expansion of the service fault diagnosis agent scene is completed by using the natural language.

The invention provides a fault positioning method and device and a fault positioning agent, and relates to the technical field of computers, in particular to the fields of artificial intelligence, cloud computing and the like. According to the specific implementation scheme, the method comprises the steps of obtaining fault description information used for describing a fault positioning task in a natural language; first diagnosis steps needing to be executed for executing the fault positioning task and a diagnosis sequence of the first diagnosis steps are planned and obtained based on the fault description information by utilizing the fault positioning agent; calling a target diagnosis tool corresponding to each first diagnosis step by using the fault positioning agent to execute each first diagnosis step according to the diagnosis sequence to obtain a diagnosis result of each first diagnosis step; and summarizing the diagnosis results of the first diagnosis steps by using the fault positioning agent to obtain an execution result of the fault positioning task.

A vehicle diagnostic system and method for identifying and presenting vehicle safety systems on a vehicle for calibration by an operator includes providing a vehicle diagnostic computer tool that includes a display screen and is configured to be connected to a diagnostic port of a vehicle. The vehicle diagnostic tool is configured to determine vehicle safety systems present on the vehicle and to receive vehicle damage information inputs corresponding to areas on the vehicle in which there is damage. A system list program generates a list of vehicle safety systems equipped on the vehicle requiring calibration based on the determined vehicle safety systems present on the vehicle and the vehicle damage information inputs, where the list corresponds to vehicle safety systems present on the vehicle that are disposed in damage areas. The vehicle diagnostic computer tool is further configured to display the list on the screen.

The invention relates to the field of disease risk prediction models, and discloses a thyroid cancer metastasis risk prediction model and a construction method and application thereof. The construction method of the model comprises the following steps: S1, collecting and preprocessing a sample; s2, gene expression quantity detection; s3, data set division and standardization processing; s4, constructing a deep learning model; s5, performing model evaluation; and the biomarker group for detecting the gene expression quantity in the S2 is composed of RPS4Y1, PKHD1L1, CRABP1, KRT18P8, AGPAT4, CPQ, SLC26A7 and RBP4. The prediction model can be used as a clinical auxiliary diagnosis tool and is used for guiding operation range selection, postoperative follow-up visit strategy making and early intervention of high-risk patients, and the precise diagnosis and treatment level of thyroid cancer is improved.

The invention belongs to the technical field of medical image analysis and artificial intelligence, and relates to an automatic medical image classification and anomaly detection system based on deep learning, and the system employs an image preprocessing module to carry out the preprocessing of a collected HSG image; inputting the preprocessed HSG image into an image classification module, and outputting an HSG image with a classification label; inputting the HSG image with the classification label into a condition guidance generation module to generate a new sample image; inputting the preprocessed HSG image and the new sample image into an anomaly detection module for anomaly detection, and visually displaying the distribution condition and severity of an abnormal area through a thermodynamic diagram display module; the invention provides the automatic, efficient and accurate image anomaly detection and positioning method, the false detection and omission ratio can be effectively reduced, the efficiency and accuracy of clinical diagnosis are improved, and a more reliable auxiliary diagnosis tool is provided for doctors.

The application relates to the field of disease prediction, and particularly relates to an epilepsy prediction system based on electroencephalogram-electrogastrogram signals collected synchronously, which comprises the following modules: a data acquisition module, which is used for acquiring data, wherein the data comprises electroencephalogram data and electrogastrogram data; a first analysis module, which is used for obtaining average electrode inconsistency indexes of first channels and second channels based on a first model according to time sequence data; and a prediction module, which is used for comparing the average electrode inconsistency indexes of a subject with preset evaluation thresholds, so as to predict epilepsy patients. The system provided by the application can more accurately and efficiently distinguish epilepsy patients from healthy people, and can be applied to the preliminary screening of epilepsy for a large population (for example, a community or a physical examination center) as an auxiliary diagnosis tool.

The application relates to the technical field of intelligent agriculture and plant protection, and particularly discloses a broccoli black spot early diagnosis system and method based on multi-modal machine learning fusion, which comprises a portable collection platform, a mobile experiment terminal and a server. The portable collection platform is integrated with a hyperspectral imaging camera, a chlorophyll fluorescence imager, a dark-adapted leaf clamp and a camera for sample barcode recognition, and is provided with an adjustable height support and a controllable light source to ensure the consistency of data collection conditions. The mobile experiment terminal is internally provided with a physicochemical index rapid detection module, including a portable PCR instrument for on-site nucleic acid rapid extraction and amplification and a miniature ultraviolet spectrophotometer for rapid determination of pigment content. The broccoli black spot early diagnosis system and method based on multi-modal machine learning fusion are highly integrated, portable and automated, are suitable for a field environment and provide an efficient disease diagnosis tool for non-expert users.

The invention discloses an interpretable machine learning method for predicting multi-organ injuries of diquat poisoning patients, and the method comprises the steps: constructing and verifying a prediction system of eight kinds of organ injuries through a clinical database; toxic encephalopathy, myocardial injury, acute kidney injury, circulatory failure, skeletal muscle injury, liver injury, lung injury and gastrointestinal tract injury are covered. According to the method, early-stage efficient prediction of multi-organ injury is achieved, deep insight is provided for potential biological mechanisms of organ injury, clinical formulation of individualized intervention strategies is facilitated, the prognosis management level of diquat poisoning patients is expected to be remarkably improved, and the incidence rate and case fatality rate of multi-organ failure are reduced. According to the technical scheme, high prediction precision is kept, meanwhile, balance of interpretability, stability and clinical applicability of the model is achieved, a reliable auxiliary diagnosis tool is provided for organ injury risk assessment of acute poisoning patients, and the method has important clinical application value and wide market prospects.

The present invention belongs to the technical field of medical image analysis and artificial intelligence, and relates to an automated medical image classification and anomaly detection system based on deep learning. An image preprocessing module is used to preprocess the collected HSG images; the preprocessed HSG images are input into an image classification module to output HSG images with classification labels; the HSG images with classification labels are input into a condition-guided generation module to generate new sample images; the preprocessed HSG images and the new sample images are input into an anomaly detection module for anomaly detection, and a heat map display module is used to intuitively display the distribution and severity of the abnormal areas; the present invention provides an automated, efficient and accurate image anomaly detection and localization method, which can effectively reduce the false detection and missed detection rates, improve the efficiency and accuracy of clinical diagnosis, and provide a more reliable auxiliary diagnosis tool for doctors.

The present invention provides a magnetic contact comprising: a stationary core magnetized by a coil; a movable core spaced apart from the stationary core by a first distance (d1) and moved by the magnetic attraction of the magnetized stationary core; a longitudinal cross bar connected to the movable core; a movable contact carrier horizontally disposed on one side of the cross bar; a movable contact disposed on one surface of the movable contact carrier; a stationary contact spaced apart from the movable contact by a second distance (d2)<sb / >; and a longitudinal contact residual quantity diagnosis tool disposed upright on the movable contact carrier so as to be adjacent to the cross bar, wherein, when over travel of the cross bar represented by the difference between the first distance and the second distance occurs, the upper side of the contact residual quantity diagnosis tool opens to the outside at the same height as the value of the over traveling.

The invention discloses a system for endometriosis diagnosis and a computer storage medium. Aiming at major adjustment of endometriosis diagnosis standards, biomarkers and ploidy information of circulating endometrial cells in easily available samples such as peripheral blood of a patient are detected, and comprehensive risk assessment is performed by utilizing an optimization algorithm model, so that the diagnosis accuracy of endometriosis is improved. The invention provides an objective and quantifiable noninvasive diagnostic tool adapted to the adjusted standard, and the method solves the problem of missed diagnosis caused by false negative in the existing surgical diagnostic method, and has important clinical application value, social benefit and market prospect for improving the overall diagnostic level of endometriosis.

We provide the system. [Solution] Information collection methods for collecting users' biometric data, A signal transmission means for transmitting the aforementioned biometric data to an augmented reality platform, The platform includes a diagnostic means that analyzes the biometric data to evaluate the user's mental state, Activity generation means that generates activities optimized for the user based on the results of the diagnostic means, A presentation means for notifying the user of the generated activity through visual or audio feedback, A feedback collection means collects user responses to the aforementioned activities again using information gathering means and reflects them in subsequent optimization, A system that includes this.

This invention discloses an apparatus and medium for an automated terminal AGV fault diagnosis system, including an AGV control unit, an AGV network connection unit, a vehicle selection unit, a task selection unit, a driving file selection unit, a task execution unit, an automatic report generation unit, a dispatch building module, a remote monitoring unit, and a remote fault diagnosis system. This invention optimizes the AGV network connection unit by establishing a 5G vehicle-mounted base station within the unit, increasing the bandwidth to 1000 Mbps to support the data flow of subsequent remote fault diagnosis tools. It also incorporates a driving process parameter recording unit and a self-developed remote fault diagnosis software tool, enabling real-time acquisition and processing of AGV driving data and remote, intelligent, and automated identification and processing of abnormal states. This lowers the technical threshold for maintenance personnel, improves the operational efficiency and accuracy of fault diagnosis, and reduces the frequency of on-site fault handling by maintenance personnel.

The invention relates to the technical field of operation and maintenance of communication operators, in particular to a method and system for complementing a positioning rule based on large model reasoning ability, and the method comprises the following steps: fault feature and tool preparation, cue word framework construction, thinking process generation and expert auditing, rule generation and current network verification, and feedback optimization mechanism. The method has the beneficial effects that by virtue of the reasoning capability of the Deepseek large model, the rule data of fault diagnosis is generated by continuously adjusting the input conditions such as the fault feature library and the fault diagnosis tool. Supplementing or perfecting a current network fault positioning rule; the problems of high expert dependency, insufficient rule credibility, difficulty in verification and the like in a traditional rule are solved, the time investment of experts is reduced, and the existing rule is perfected; an efficient, credible and iterable fault positioning mechanism is realized, and the intelligent level of network operation and maintenance is improved.

The invention discloses a diagnosis method and device for a mobile network 5G voice service complaint. The method comprises the following steps: obtaining a complaint problem of a mobile network 5G voice service complaint and a telephone number corresponding to a complaint user; according to the complaint question and the telephone number, inquiring contract signing and registration information of the complaint user in a core network to obtain user contract signing and registration information; and inputting the complaint problem and the user signing registration information into an expert database model to obtain a diagnosis result and a diagnosis suggestion so as to complete the diagnosis of the mobile network 5G voice service complaint, wherein the expert database model is an intelligent diagnosis tool which is pre-constructed on the basis of logic rules set by expert experience in combination with the influence of complaint scenes, network element priorities and contract signing functions on the 5G voice service of the mobile network. According to the method, the 5G voice service complaint in the mobile network can be automatically diagnosed, manual intervention is not needed, the processing efficiency is remarkably improved, and the requirements of a user can be rapidly met.

A system is provided.SOLUTION: A system comprising: diagnostic means for diagnosing a technical level of a user; teaching material providing means for providing a teaching material and a task suitable for the user based on a result of the diagnostic means; evaluation means for evaluating a result of the task and generating feedback; and progress management means for managing learning progress of the user and providing a message and a reward for maintaining motivation.SELECTED DRAWING: Figure 1

The invention is suitable for the technical field of medical intelligent diagnosis and machine learning, and provides a pneumonia screening and risk prediction model construction method based on a reverse neural network, and the method comprises the steps: firstly carrying out the preprocessing of a bacterial pneumonia clinical symptom data set, and dividing a multi-classification problem into a plurality of binary classification tasks; secondly, constructing a CPT algorithm based on a random forest and a convex hull theory, calculating a feature weight through information gain, and screening out high-contribution symptom features; and finally, inputting the screened features into the optimized BPNN, and obtaining a prediction model through repeated iterative training. The method has good classification accuracy, the number of the selected features is obviously reduced compared with a traditional model, and the method can be used as a clinical auxiliary diagnosis tool, is applied to clinical bacterial pneumonia infection screening and severe risk early warning, and provides decision support for timely treatment.

The invention relates to the field of medical auxiliary diagnosis, in particular to a tool calling method and system for medical auxiliary diagnosis, and the method comprises the steps: collecting historical case data from a medical information system, carrying out the preprocessing, generating an empirical data set, receiving the current symptom information of a patient, and carrying out the semantic analysis of the current symptom information, extracting scene features of the current symptom segmented words, building a tool matching model, inputting the current symptom segmented words and the scene features into the tool matching model, outputting a corresponding auxiliary diagnosis tool type, and calling a corresponding auxiliary diagnosis tool according to the auxiliary diagnosis tool type. And the current symptom information of the patient is transmitted to the auxiliary diagnosis tool to generate the diagnosis suggestion, so that the historical case data and the current symptom information of the patient are effectively integrated, the auxiliary diagnosis tool currently corresponding to the patient is accurately matched, the diagnosis efficiency and accuracy are improved, and powerful support is provided for medical diagnosis.

The invention provides a multi-target joint detection kit for prostatic cancer, a detection system and application, multiple targets in the kit are selected from a PCAT1 gene, a PSA gene, a TTC3 gene, an EPCAM gene and an MIR4435 gene, and the kit further comprises a primer and probe combination aiming at each marker. According to the method, the diagnosis accuracy can be improved, the weight interpretation model can be constructed, the clinical decision can be optimized, and a non-invasive and high-compliance auxiliary diagnosis tool is provided for the problems of traumatic detection and gray areas in the prior art.

The invention relates to the technical field of family trust, and discloses a family enterprise governance cooperation management and control system, which comprises a service product management module used for managing service products of family trust; the service execution management module is used for managing execution of service products; the asset list input module is used for inputting asset data of the customer; the risk diagnosis module is used for diagnosing customer risks; the file management module is used for encrypting and managing files and data which are uploaded by the user and related to the specific customer in a digital file form; the system can help an enterprise to realize customer full life cycle management including customer data collection, customer follow-up, sales opportunity tracking, auxiliary order talking, project management, expert resource combination and the like, has a powerful customer property risk diagnosis tool and a block chain encryption technology, can help the enterprise to improve the sales efficiency, and has a good market prospect. And a privacy data security solution is provided for the client.

The invention discloses a sarcopenia assessment system and medical equipment, and the system comprises a data processing module which is used for splitting and preprocessing clinical data; the feature screening module is used for determining an optimal feature subset; the model building and training module is used for building and optimizing a sarcopenia evaluation model; the model evaluation module is used for evaluating the generalization performance of the model; and the practical application module is used for evaluating a new patient. Based on a bioelectrical impedance analysis technology, four key characteristics of gender, body weight, extracellular fluid resistance and intracellular fluid resistance are automatically screened out, and a multi-model preferential strategy is adopted to construct a high-performance evaluation model. According to the method, the defects that a traditional method depends on expensive equipment, complicated processes, subjective indexes and the like are overcome, rapid, objective and accurate screening of sarcopenia is achieved, and an efficient and reliable auxiliary diagnosis tool is provided for clinic.

The present application relates to the field of disease prediction, and particularly relates to a cognitive impairment prediction system based on synchronous acquisition of electroencephalogram and electro-gastrogram signals, comprising: a data acquisition module, configured to acquire data, wherein the data comprises electroencephalogram data and electro-gastrogram data; a first analysis module, configured to obtain average electrode inconsistency indexes of first and second channels based on a first model according to time series data; and a prediction module, configured to compare the average electrode inconsistency indexes of the subject with preset evaluation thresholds, thereby predicting cognitive impairment patients. The system provided by the present application can more accurately and efficiently distinguish mild cognitive impairment patients from healthy people, and can be applied as an auxiliary diagnosis tool for the initial screening of mild cognitive impairment in a large population (for example, a community or a physical examination center).

A disease diagnosis tool validates a diagnosis of a patient. The disease diagnosis tool receives a high-resolution image of a body part. The disease diagnosis tool divides the high-resolution image into a plurality of tiles and inputs a representation of each tile into an encoder portion of a model configured to perform a disease diagnosis based on the representations. The encoder has an attention mechanism. The disease diagnosis tool obtains a plurality of tokens representative of an attention of the encoder based on the attention mechanism. Each token is associated with a position of a tile of the plurality of tiles. The disease diagnosis tool generates a heat map corresponding to the image of the body part. The heat map comprises a two-dimensional image having pixels corresponding to tiles each with an amplitude based on a level of attention represented in the plurality of tokens corresponding to the tiles.

The invention discloses a gene marker combination, a kit and a system for breast cancer molecular typing, and belongs to the field of precise medical big data analysis. The gene marker combination is composed of 17 gene markers, namely, CLDN19, DSG1, FABP7, FGFBP1, KLK7, KLK8, MAPK4, MAPT-AS1, MAPT-IT1, MIA, PTPRZ1, ROPN1, ROPN1B, SCN2B, SERPINA11, SOX11, and SRARP. The invention further discloses a preparation method of the gene marker combination. Based on the combination, a brand new breast cancer GPS17 molecular typing system is constructed, the breast cancer can be finely divided into eight molecular subtypes with remarkable expression difference, and at least four different molecular subgroups in the Luminal A subtype are systematically analyzed for the first time. Experiments show that the molecular typing system is obviously superior to a traditional PAM50 molecular typing system in balance accuracy, macro F1 score and macro AUC score. The invention further provides a corresponding detection kit and an automatic typing system integrated with a machine learning model, and an auxiliary diagnosis tool with excellent performance is provided for accurate diagnosis and treatment decision of breast cancer.