59 results about "Diseases database" patented technology

The invention provides a nervous system closed-loop regulation and control system based on transcranial opto-acoustic cooperation. The nervous system closed-loop regulation and control system is composed of EEG monitoring equipment, personal information data, an opto-acoustic cooperation system, a multi-mode large model and a psychological disease database. Aiming at a transcranial infrared light and ultrasonic co-stimulation technology of a patient, through optimization of stimulation parameters, photoacoustic bidirectional regulation and control are realized, a multi-modal large model is used as a base, multi-modal data fusion and reinforcement learning are used as technical means, closed-loop photoacoustic co-stimulation system equipment is designed, and intervention parameters are automatically optimized; an effective regulation and control method for psychological diseases is realized through a non-intrusive brain stimulation technology, and adaptive adjustment and closed-loop feedback of parameters are realized. Brain waves of a patient are detected in real time, personal information of the patient is collected, the information is integrated and transmitted into the system after evaluation, intervention is adjusted in real time according to different brain states, and feedback is utilized to adjust parameters of next intervention and stimulate the precise regulation and control process.

The invention relates to the field of image cultural relic disease recognition and evaluation, in particular to a cultural relic disease recognition and evaluation system and method based on image analysis, and the system comprises a multispectral image collection module, an image processing module, a disease recognition module, a repair evaluation module, a decision optimization module and a disease database. The multispectral image acquisition module optimizes a light source through a reflective area detection algorithm to obtain a multispectral image; the image processing module uses a multi-band collaborative filtering algorithm to eliminate noise and extract fusion features, and divides disease areas; the disease identification module determines disease positions through a connected region marking algorithm, and performs disease classification by means of a neural symbol model; the repair evaluation module outputs a quantitative score; the decision optimization module weighs the repair effect, the cost and the time by introducing a multi-objective optimization model of a genetic algorithm, and outputs an optimal scheme; according to the invention, the disease identification precision and the automation and scientificity of repair evaluation are improved.

The invention discloses a CKD special disease database construction method and system, and relates to the technical field of database construction, and the method comprises the steps: obtaining and predicating a multi-source heterogeneous atomic fact; constructing and fusing three sets of ontology models; verifying and deducing facts in a layered manner according to a multi-layer priority rule base; calling a constraint completion engine to generate inference facts for missing or conflicting fields; streaming follow-up visit, test and intervention logs into events and triggering graded alarms; an anti-fact intervention scene is simulated for the high-risk patient, and pilot groups are screened; dynamically evaluating and adjusting the rule priority; and based on a causal chain and an event mode, clustering patients and constructing a generality map, and recording a whole-process operation chain. Semantic management is realized through fusion of ontology, anti-fact intervention simulation is performed on high-risk patients by means of multi-layer rule verification, and database semantic consistency, reasoning depth, decision support and traceability are improved through causal atlas driven clustering.

The invention relates to the technical field of bridge disease diagnosis and maintenance, in particular to a bridge disease diagnosis and maintenance measure recommendation method which comprises the following steps: constructing a bridge disease database which comprises feature data and cause data of various types of bridge diseases and maintenance measure data matched with the various types of diseases; receiving field disease information of a target bridge input by a user, wherein the field disease information comprises a disease type and a disease characteristic parameter; performing matching analysis on the field disease information and data in the bridge disease database, and diagnosing a disease cause of the target bridge based on a matching result; according to the diagnosed disease causes, one or more maintenance measures corresponding to the disease causes are called from the bridge disease database and output as recommended schemes, and a complete data link from disease detection to maintenance decision is established by constructing the standardized bridge disease database and a matching mechanism.

The invention discloses an unmanned aerial vehicle bridge disease inspection method and system based on DeepSeek-YOLO target detection. The method comprises the following steps: S100, planning a flight path of an unmanned aerial vehicle; s200, a unified time reference is provided for all the sensors, and hardware trigger signal synchronization is carried out; s300, the unmanned aerial vehicle flies according to the planned route, and the multi-sensor collaborative acquisition module acquires data at the same time; s400, processing the collected data, fusing the processed data, and constructing a bridge disease database; s500, establishing a YOLO target detection model, and optimizing the YOLO target detection model through the convolutional neural network in combination with DeepSeek; s600, performing disease identification by using a YOLO target detection model to obtain bridge disease information; s700, selecting multi-dimensional data, and generating a three-dimensional model of the bridge; s800, outputting an image in the digital twinborn visualization platform; and S900, updating the three-dimensional model in real time according to bridge disease information, comparing an identified disease result with an expert label, and feeding back the result to the YOLO target detection model to further train the YOLO target detection model.

The invention relates to a rail transit infrastructure intelligent operation and maintenance method and system based on a digital twinborn technology. According to the method, a finite element and multi-body dynamics joint simulation method is adopted, a vehicle-road coupling calculation model is established, and the calculation result of the vehicle-road coupling calculation model and real-time data of a sensor are integrated to form a disease database; based on the dynamic characteristics in the disease database, training to obtain a disease damage identification model; secondly, digital twinning modeling is conducted on the rail transit infrastructure, then a rail transit digital twinning intelligent operation and maintenance front end is constructed, and in the operation and maintenance process, the operation and maintenance front end sends a disease prediction request to the disease damage identification model; and after the request is received, the model reads real-time data of the sensor in the disease database, performs damage identification to generate a real-time prediction result, and returns the real-time prediction result to the operation and maintenance front end. Compared with the prior art, the method has the advantages of being stable, high in adaptability, good in prediction capability, high in working efficiency and the like.

The invention relates to an intelligent hospital management system based on clinical scientific research integration, and the system comprises a clinical scientific research integration platform which is provided with a special disease database data service module which is used for integrating patient clinical data with a patient as a center; the special disease database data service module comprises a special disease scientific research data center which is used for storing high-quality multi-dimensional structured patient data processed by an artificial intelligence technology; the regional decision application module is used for collecting multi-hospital-area data and carrying out hospital diagnosis and treatment and multi-dimensional analysis of medication data; the regional collaborative application module is used for realizing medical resource sharing of multiple hospital areas; and the regional multi-center-line image integration module is used for acquiring image system data of a plurality of courtyards and realizing sharing and mutual recognition. According to the invention, multi-center medical data integration and scientific research collaboration are realized, and the collaborative work and collaborative diagnosis and treatment efficiency is improved.

The invention relates to an intelligent hospital guide method and system, an intelligent terminal and a storage medium, and relates to the field of intelligent medical treatment, and the method comprises the steps: collecting the case information of a patient, the case information comprising medical history information and symptom description; performing data processing on the case information by using an illness state analysis model to obtain illness state characteristics of the patient; potential diseases of the patient are retrieved in a disease database according to the illness state characteristics; determining a department and a hanging suggestion corresponding to the potential disease; screening out recommended doctors from a doctor qualification database according to the department; and pushing the patient hanging suggestion and registration information corresponding to the recommended doctor to a user terminal of the patient. The method has the effect of improving the accuracy and efficiency of medical triage.

The invention relates to the field of computer information processing, in particular to a multi-source heterogeneous knowledge graph construction method and system. The method comprises the steps that ancient books, clinical literatures and traditional Chinese medicine, medicine and disease database data are accessed, and entity candidate sets are formed after field mapping extraction; calculating a name similarity, an attribute similarity and a semantic vector similarity for the entity candidate set, and inputting multiple classes of similarities into a fusion network to generate an entity alignment mapping table; constructing a remote supervision annotation set and training a relationship classification model, driving expert annotation increment update through model uncertainty, and outputting a relationship triple set and a corresponding relationship confidence set; and finally, forming a three-layer heterogeneous graph structure communicated with the syndrome type, the disease, the prescription and the medicinal components, and writing the three-layer heterogeneous graph structure into a graph database to generate a knowledge graph database. Unified modeling of entity alignment, relation extraction and relation strength quantification in the multi-source heterogeneous data is achieved, and the structural consistency in the knowledge graph construction process is improved.

The invention discloses an intelligent customer service implementation method of a medicine knowledge map based on an AI large model. The method comprises the steps of extracting preliminary semantic information from a medicine specification, a user health file and a disease database through a DeepSeek model; performing dynamic weight adjustment on each extracted preliminary semantic result to obtain a comprehensive score of each preliminary semantic extraction result; when the comprehensive score exceeds a preset threshold value, a triple corresponding to an extraction result corresponding to the comprehensive score is included into a final medicine knowledge graph, automatic construction of a question sentence of a user is achieved by adopting a generative method based on symptom description and a large model DeepSeek in a medicine knowledge sub-graph, and meanwhile medicine association matching is completed. The question and answer pair data (q, D) are formed, q is an automatically generated question, and D is a drug set related to the question. Therefore, efficient bidirectional interaction between the large model and the knowledge graph is realized, and the analysis efficiency of complex medical problems is improved.

The invention relates to the technical field of bridge equipment, and discloses an expansion joint monitoring device and method based on audio and video, and the method comprises the following steps: 1, collecting the image information of a target point location and the audio information of a target scene, and forming a disease database; the target point location comprises an expansion joint on the bridge and a cover beam at the position; the target scene comprises a scene in which the vehicle travels through the expansion joint; step 2, based on the disease database, dividing disease grades of the expansion joint according to a preset rule; according to the disease grade, the monitoring frequency required by the expansion joint is determined; and step 3, monitoring the expansion joint according to the monitoring frequency, analyzing the disease development condition of the expansion joint according to the monitoring condition, adjusting the monitoring frequency when the disease development condition shows that the disease grade is upgraded, and outputting a maintenance suggestion. According to the invention, efficient, accurate and safe expansion joint monitoring can be realized, the bridge inspection and maintenance efficiency can be improved, and the safety risk can be reduced.

The invention relates to the technical field of medical data management, and discloses a GIST special disease database intelligent management system, which comprises a multi-source data acquisition module, a standardized processing module, an intelligent analysis module and a security sharing module, the multi-source data acquisition module is in communication connection with a hospital information system H IS, a laboratory information system L IS, an image archiving and communication system PACS and a patient end application, and is used for acquiring electronic medical records, inspection reports, image data and patient active report data; a standardized data model is built in the standardized processing module, and the standardized processing module is used for performing field mapping and format conversion on the acquired multi-modal data; the intelligent analysis module comprises a machine learning algorithm engine. According to the medical data management system, the multi-source data acquisition module is in communication connection with the hospital information system and the laboratory system, so that the problem that most traditional medical data management systems can only acquire single-source or single-mode data, and consequently disease characteristics cannot be comprehensively reflected is solved.

The application provides a multi-source Parkinson's disease database integration method based on LEDD-UPDRS composite index alignment, and is applied to the technical field of data processing. In view of the heterogeneous problems of multi-source Parkinson's disease databases, the application integrates heterogeneous data of PPMI, PDBP and local queues, disassembles characteristics according to four modes of heredity and clinic, introduces a standardized term library and a BERT-BiLSTM-CRF model unified term adjusted in combination with medical corpus, generates a cross-library feature table; a storage architecture is constructed based on FlashROM mode to realize data storage and retrieval; a composite observation system is built with LEDD and UPDRS-III, consistency is verified by multiple methods, data is standardized, missing data is filled in combination with multiple models to generate an integrated data set; a double prediction model is built relying on the data set, and the advantages of the data are realized by a Cohen's d value verification method; finally, cross-library joint analysis is carried out, phenotype-genotype correlation modeling and causal inference are completed, and data support is provided for Parkinson's disease research and clinical prediction.

The invention provides a data processing method, medical equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring a target gestational week stage of a target fetus, and determining a plurality of disease categories corresponding to the target gestational week stage in a disease category database based on the target gestational week stage; acquiring disease characterization information of the target fetus, wherein the disease characterization information comprises one or more of a target section image and a disease description text of the target fetus; and for each disease category corresponding to the target gestational week stage, based on the disease characterization information of the target fetus, calculating the disease probability that the target fetus suffers from the disease of the disease category. And based on each disease probability, determining a final disease category in a plurality of disease categories corresponding to the target gestational week stage, and outputting a standard disease name corresponding to the final disease category. According to the invention, the realization difficulty of subsequent medical data statistics can be reduced.

A method of automated diagnosis of disease database entities includes receiving a case processing request via an input application programming interface (API), extracting image data from the case processing request including at least one medical scan image of the patient, selecting at least a portion of the medical scan image(s) according to specified selection criteria, normalizing the selected at least a portion of the medical scan image(s), supplying the selected at least a portion of the medical scan image(s) to a machine learning model to generate a target medical condition prediction output, wherein the target medical condition prediction output is indicative of a likelihood that a patient will experience a future disease diagnosis event corresponding to the target medical condition, and automatically transmitting the target medical condition prediction output as an electronic transmission via an output API to a provider system associated with the patient.

The invention discloses a gene detection management method and system, which comprises a complete process from biological sample collection, biological sample treatment, gene detection, data analysis to result report, realizes multi-level feature extraction, also comprises extraction and analysis of various biological information of genome DNA, RNA, protein, miRNA and lncRNA, improves the depth of gene detection, and improves the detection efficiency. According to the method, a gene-disease database and optimized feature selection are combined in reality, gene variation features with the strongest relevance with diseases are recognized, the gene-disease database is continuously updated and accurate through continuous feature selection, regression model training and verification processes, the prediction level of relevance between genes and diseases is improved, and the prediction efficiency is improved. A probability distribution diagram is generated by monitoring the difference between a database and a verification set, it is ensured that the gene-disease database is stably converged to an accurate and credible level as time goes on, a personalized site variation analysis report is automatically generated according to an analysis result, and an accurate seed selection basis is provided for breeding experts.

A method for processing game information, including: obtaining a target disease in a game of a virtual city, where the target disease comprises at least one disease in a disease database; determining a current transmission target disease in the virtual city according to a trigger probability and a trigger interval of the target disease, where the current transmission target disease is set to transmit in the virtual city at a preset rate; displaying a crisis early warning task corresponding to the current transmission target disease on the graphical user interface, where the crisis early warning task includes a disease name of the current transmission target disease and corresponding task information; and reducing, in response to completing the task information, the preset rate of transmission of the current transmission target disease in the virtual city.

The embodiment of the invention provides a railway station building typical disease intelligent identification method based on AutoML, and the method comprises the following steps: collecting video image data of railway station building typical diseases, and carrying out the preprocessing of contrast enhancement and size standardization of the video image data, the typical diseases comprise structural connecting piece defects, metal structure surface defects and decorative plate defects; constructing a training data set based on the preprocessed image data, and performing automatic structure search and hyper-parameter tuning on the image recognition model by using an AutoML automatic machine learning framework to obtain an optimized image recognition model; inputting a to-be-recognized railway station building image into the optimized image recognition model for disease recognition, and outputting a classification recognition result; and matching the classification identification result with historical data in a railway station building disease database, and outputting final disease type information according to a matching result.

The invention relates to the technical field of digital data processing, in particular to an online inquiry information processing method and system based on big data, and the method comprises the steps: obtaining basic illness state description information inputted by a patient; analyzing the basic illness state description information by using a natural language processing technology, and retrieving all related disease names in a disease database; all corresponding disease symptoms in a database are retrieved based on the disease names, the disease symptoms are sorted according to correlation scores of personal characteristics in the database, and the personal characteristics comprise gender, age and residential areas; presenting the sorted disease symptom list to the patient, and obtaining second illness state information which is selected by the patient and conforms to the own condition; and generating online inquiry information based on the basic illness state description information and the second illness state information. Therefore, the accessibility and response speed of medical services are improved, and the accuracy and completeness of information provided by patients are enhanced through effective utilization of big data.

An information processing technology capable of increasing prediction accuracy of causing cardiac diseases, performing appropriate therapy and increasing efficiency of drug development is constructed by utilizing “in-silico cardiac disease database.” An electrocardiogram and the like obtained from “in-silico cardiac disease database” which is the database storing a simulation result of a cardiac model of virtual disease where various factor related to cardiac disease are changed are inputted to a classifier for performing an automatic diagnosis of the cardiac disease, and an influencing factor associated with the cardiac disease is identified as a biomarker by comparing distributions of a variation amount of a factor between a positive group and a negative group. In addition, precise diagnosis of an individual is easily performed by identifying the cardiac model having the electrocardiogram and the like appropriate to the actual electrocardiogram and the like of the individual in the in-silico cardiac disease database. Furthermore, the process of drug development is efficiently performed by extracting a group having a predetermined feature from the in-silico cardiac disease database and simply evaluating an effect of an administration of a medical agent.

The present invention relates to a system for obtaining a disease database based on an NLP system. The system includes: a processor and a memory storing a computer program. When the computer program is executed by the processor, the following steps are implemented: obtaining an initial medical record text set, obtaining an intermediate medical record text set according to the initial medical record text set, inputting the intermediate medical record text set into a preset NLP system to obtain an intermediate event knowledge graph corresponding to the intermediate medical record text set, and obtaining a disease database according to a preset intermediate rule list and the intermediate event knowledge graph. Above, by combining the NLP system with the rules, the data in the medical record text can be understood and analyzed more accurately, improving the accuracy and efficiency of obtaining the disease database. The NLP system can provide an understanding and analysis of the medical record text data, providing richer and more accurate information for the rules, resulting in a relatively high accuracy of the obtained disease database.

The present invention discloses a traditional Chinese medicine intelligent consultation method and system based on an AI large language model. The system includes a data acquisition module, an image processing module, a semantic analysis module, a disease database management module, a multimodal fusion module, a neural network retrieval module, an interaction optimization module, and an output module. The intelligent consultation method includes obtaining facial images, body images, and tongue coating images of a patient; constructing a disease database; based on the image data, collecting the voice responses of the patient, extracting the semantic content and language breath features in the voice, and the language breath features include speech rate, pitch, and pause frequency; converting the semantic content of the patient into traditional Chinese medicine professional terms, associating with the synonym list in the disease database, and normalizing inconsistent terms; fusing the image data, normalized terms, and language breath features through a multimodal neural network model; based on the comprehensive feature vector, performing a difference analysis on the remaining symptoms of the candidate diseases; and outputting the final diagnosis result.

The application discloses an optimal integrated system and method for realizing real-time supervision of a continuous deep learning model, comprising a continuous learning subsystem, a data processing module, an optimal model generation module, a diagnosis module, a data storage module, a pathological type database, a pathological disease database, an optimal model database and an ultrasonic PACS subsystem, and the realization method comprises the following steps: step one, data set construction; step two, algorithm optimization; step three, system development; and step four, system application.The integrated system is designed by using the model optimization method, realizes supervision of the model, and can guarantee the stability of the diagnosis performance of the integrated system.The integrated system has the continuous learning capability, the diagnosis capability thereof is continuously improved along with stage learning, and the accuracy of the diagnosis result is guaranteed.The integrated system can output multiple diagnosis results by using the model integrated design, and plays a very important auxiliary role in disease identification for doctors.

The invention discloses a multi-center special disease database system construction method, which comprises the following steps: S1, establishing a data collection center, and acquiring detection and treatment text data of a plurality of hospitals on the same type of special diseases through the data collection center; s2, establishing an evaluation objective function by taking a treatment result in the treatment text data as a guide and selecting a cure rate, a treatment cycle and other treatment results as standards for the same type of special diseases; s3, selecting treatment text data corresponding to the optimal treatment result as treatment standard data according to the established evaluation objective function, and storing the treatment text data in a database; s4, taking detection text data corresponding to the treatment text data as detection reference file data of the same type of special diseases, and storing the detection text data into a database; and S5, carrying out statistics on the detection reference file data of the same type of special diseases, summarizing a detection reference data value range of the same type of special diseases, and issuing the detection reference data value range to a hospital terminal for a plurality of hospitals to detect reference indexes of the same type of special diseases.

The present invention provides a data processing method, medical equipment, storage medium and computer program product. The above method includes: obtaining a target gestational age stage of a target fetus, and based on the target gestational age stage, determining multiple disease categories corresponding to the target gestational age stage in a disease database. Obtaining disease characterization information of the target fetus, the disease characterization information includes one or more of a target cross-sectional image of the target fetus and a disease description text. For each disease category corresponding to the target gestational age stage, the probability of the target fetus suffering from a disease of the disease category is calculated based on the disease characterization information of the target fetus. Based on each disease probability, a final disease category is determined from the multiple disease categories corresponding to the target gestational age stage, and the corresponding standard disease name is output. The present invention is conducive to reducing the difficulty of implementing subsequent medical data statistics.

The invention relates to the technical field of intelligent hospital guide, in particular to an intelligent hospital guide system based on cloud computing, which comprises a data acquisition port, a data matching port and a disease tracing port, and is characterized in that the data acquisition port is used for acquiring symptom description, medical history, basic physiological indexes, age, gender and patient behavior logs of a patient in real time through multi-source input; the data matching port is used for comparing patient symptoms with a disease database, calculating a symptom matching degree and screening potential diseases; and the disease tracing port is used for locating the most probable disease or department and generating a hospital guide suggestion.

The invention discloses a sweet potato disease early warning device based on a remote sensing technology, and relates to the technical field of crop planting, and the device comprises a processing module which divides a planting area into a plurality of recognition areas, and comprises a plurality of sub-processing units and an analysis unit; each sub-processing unit is matched with each identification area; the analysis unit is internally provided with a disease database, and the disease database is marked with a plurality of identification features based on the features of each sweet potato disease; the data monitoring module comprises a plurality of soil monitoring units; each soil monitoring unit is respectively matched with each identification area, monitors soil nutrient substances and soil environment in the corresponding identification area to obtain corresponding monitoring data, and transmits the monitoring data to the corresponding sub-processing unit; according to the method, the real-time remote sensing data of different identification areas are periodically detected, and disease symptoms can be timely found in the early stage of sweet potato diseases, so that early warning is realized.

The invention provides a health medical data analysis method, electronic equipment and a storage medium, and relates to the technical field of data processing. The method comprises the following steps: acquiring a first encrypted health medical data set through a target special disease database according to a target disease identifier and a target analysis object identifier submitted by a first medicine request end; obtaining a first analysis mirror image according to application scene information submitted by the first medicine request end, constructing a first security container according to the first encrypted health medical data set, decrypting the first encrypted health medical data set in the first security container, and sending the decrypted first encrypted health medical data set to the first medicine request end; the first analysis mirror image is operated to carry out medical data analysis on the decrypted second health medical data set, and a medical data analysis report is output, so that a medical research user can carry out medical data analysis based on the health medical data of a real patient; the curative effect, the safety and the long-term effect of the medicine in actual clinical application can be reflected, and the privacy security of medical data can be ensured.

The present invention discloses a method for constructing a dietary recommendation model based on a diet-gut microbiota-disease database, belonging to the technical fields of databases and dietary recommendations. The database of the present invention is constructed by integrating relevant open-source databases and literature, and the content of the database is standardized. The method for constructing the dietary recommendation model includes the following steps: Step 1: Based on the database, modeling is carried out through semantic similarity, Gaussian kernel profile similarity calculation, and graph attention network to construct a diet-gut microbiota and gut microbiota-disease correlation network; Step 2: Based on the qualitative relationships in the database, corresponding to the nodes in the correlation network, a diet-gut microbiota-disease network is constructed; Step 3: The target screening of gut microbiota is carried out through a classifier-optimized model to obtain dietary recommendation results for different diseases. The present invention can provide a data support means in the field of diet-gut microbiota-disease and provide dietary suggestions according to the human health disease status.

The present invention provides a method, system, device and storage medium for producing a disease-specific database, which belongs to the field of medical disease-specific databases. The method can obtain a first recognition result by identifying entities and relationships in target electronic medical records traversed by a deep model; extract entities required for the fields of the target disease-specific database from the first recognition result, and if it can be proposed, write the proposed entity content into the target disease-specific database; otherwise, use a machine learning model to identify the traversed target electronic medical records to obtain a second recognition result, then fuse the first recognition result and the second recognition result to obtain a final recognition result and extract entities required for the fields of the target disease-specific database, and if it can be proposed, write the extracted entity content into the target disease-specific database; otherwise, use field data to retrain the machine learning model to obtain a new machine learning model, and then produce a disease-specific database based on the new machine learning model and the deep model. The present invention is used to avoid retraining of deep models, reduce costs, and increase flexibility of use.