37 results about "Diseases database" patented technology

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 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 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.

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.

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 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 invention belongs to the technical field of pavement damage detection systems, and particularly relates to a pavement damage detection system and method based on artificial intelligence image recognition. An algorithm integration module is used for integrating an artificial intelligence image recognition algorithm and developing and forming efficient automatic recognition software; the function decoupling module is used for decoupling a software interaction function and an algorithm recognition function through a preset standardized interface and supporting a user to autonomously deploy, update and replace an algorithm, and the equipment adaptation module is used for adapting to data folder formats and stake mark correction logics of at least two detection vehicles. And the data management module is used for carrying out disease type labeling on field acquired picture data to form a disease labeling picture data set and storing a recognition result and manual correction data into a disease database, and comprises a data updating unit for updating the database at regular intervals, or supporting a user to autonomously construct a data file so as to realize image recognition. And the data management module is used for carrying out disease type labeling on the field acquired picture data to form a disease labeling picture data set and storing a recognition result and manual correction data into the disease database.

The application belongs to the technical field of semantic processing, and discloses an ERAS nursing special disease database construction method and system based on semantic processing, which comprises the following steps: receiving multi-source data from nursing records, medical order information and a preset ERAS nursing path node, uniformly processing the multi-source data in a format and field standardization manner, classifying and arranging nursing related information, and forming a standardized nursing data set; performing semantic analysis on the standardized nursing data set, identifying nursing semantic elements, constructing corresponding nursing event objects based on the nursing semantic elements, and forming a condition record set associated with each nursing event object; calculating a semantic certainty score for each nursing event object in the condition record set, wherein the semantic certainty score is composed of a semantic anchor certainty, a rule inference certainty and a context coverage certainty; and the accuracy and intelligent level of the ERAS nursing special disease database in data modeling, query screening and decision support are improved.

The application discloses a kind of data processing methods of road surface disease image fusion based on three-dimensional digital holography, and original graph is obtained by generating digital interference fringe image and projecting to road surface through laser interferometer and collecting road surface;Computer simulation restoration reconstruction is obtained three-dimensional holographic disease graph;Two-dimensional disease graph is obtained using Fourier transform;Three-dimensional holographic disease graph and two-dimensional disease graph are fused to generate fusion disease graph;Using specified data, the final training model is obtained by testing simulation correction to fusion model.This application can obtain three-dimensional disease graph and two-dimensional disease graph according to original hologram, and through least square method, left and right and before and after disease graph are spliced to form multi-size disease database, the combination of two-dimensional and three-dimensional disease graph data not only greatly improves data precision, but also improves image quality, and disease detection type covers various diseases of road surface, which can facilitate equipment to automatically identify road surface disease.

The invention provides a disease auxiliary diagnosis system, a model using method and a training method, and the system comprises a disease auxiliary diagnosis model, the model comprises an input module which is used for inputting a symptom set, and the symptom set comprises at least one input symptom; the preprocessing module is used for preprocessing at least one input symptom in the symptom set to obtain a corresponding processed symptom; the symptom grouping module is used for performing associated symptom clustering based on the corresponding treatment symptoms and a disease database to obtain at least one symptom group; the enhanced reasoning module is used for performing disease prediction based on the at least one symptom group to obtain a disease prediction result; and the output module is used for outputting a visual report. In this way, the technical problems that in the prior art, the accuracy of a prediction result is low, the prediction result lacks interpretability, and the treatment efficiency is affected can be solved.

The invention discloses a method for screening action targets of Zuogui pills for treating ovarian dysfunction based on high-throughput omics and network integration, and the target screening method comprises the following steps: screening differential expression proteins by using proteomics, and taking an intersection of the differential expression proteins and drug and disease database targets to construct a database-experimental target library; core targets are screened through drug-component-target-disease multi-dimensional network analysis in combination with a database, experimental data, network node degree and target occurrence frequency in function enrichment analysis, and accurate positioning of the action targets of the Zuogui pill for treating the ovarian dysfunction is achieved.

This application proposes a method, apparatus, electronic device, storage medium, and product for filling in a disease-specific database. The method, based on a pre-built medical instruction knowledge base and pre-collected medical data of patients with specific diseases, determines data extraction instructions corresponding to the items to be filled in the disease-specific database. The medical instruction base includes data extraction instruction templates corresponding to the items to be filled. By executing the data extraction instructions, data corresponding to the items to be filled in the disease-specific database is extracted from the medical data of patients with specific diseases. The data is then filled into the items to be filled in the disease-specific database. Using the technical solution of this application, the automatic acquisition and automatic filling of data corresponding to the items to be filled in the disease-specific database can be achieved, improving the efficiency and accuracy of disease-specific database filling.

The present invention relates to the field of image-based cultural relic disease identification and assessment, and specifically relates to a cultural relic disease identification and assessment system and method based on image analysis. The system comprises a multispectral image acquisition module, an image processing module, a disease identification module, a restoration assessment module, a decision optimization module, and a disease database. The multispectral image acquisition module optimizes the light source by using a reflective area detection algorithm to acquire a multispectral image. The image processing module uses a multi-band collaborative filtering algorithm to eliminate noise, extract fusion features, and divide disease areas. The disease identification module uses a connected region labeling algorithm to determine the location of the disease and classifies the disease with the help of a neural symbolic model. The restoration assessment module outputs a quantitative score. The decision optimization module uses a multi-objective optimization model introduced by a genetic algorithm to weigh the restoration effect, cost, and time, and output an optimal solution. The present invention improves the accuracy of disease identification and the automation and scientific nature of restoration assessment.

The present application relates to the technical field of medical information processing, and discloses a multi-level medical record information extraction method, device, equipment and storage medium, the method comprising: obtaining original unstructured medical record text, and cutting the original unstructured medical record text into at least one text block based on title semantic features and a preset text length threshold; dividing full data fields of a target special disease database into a plurality of mutually disjoint small batch field groups, extracting field metadata to construct prompt words, inputting the text block and the prompt words into a large model to obtain a structured data segment; traversing all text blocks and field groups, performing conflict detection based on a unified field identifier, using a multi-source timestamp priority strategy to complete conflict resolution, and fusing to generate a complete patient structured portrait and storing the same. The present application can improve the accuracy and stability of medical record information extraction, guarantee data quality homogeneity, and flexibly adapt to the extraction requirements of different special diseases.

The present application relates to the field of computer information processing, and particularly relates to a multi-source heterogeneous knowledge graph construction method and system. The method comprises the following steps: accessing ancient books, clinical literature, and data of traditional Chinese medicine, drugs and disease databases, and forming an entity candidate set after field mapping extraction; calculating the name similarity, attribute similarity and semantic vector similarity of the entity candidate set, and inputting the multiple types of similarity 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 incremental update by model uncertainty, and outputting a relationship triple set and a corresponding relationship confidence set; finally forming a three-layer heterogeneous graph structure connected by syndrome type, disease, prescription and drug components, and writing into a graph database to generate a knowledge graph database. The present application realizes unified modeling of entity alignment, relationship extraction and relationship strength quantification in multi-source heterogeneous data, and improves the structural consistency of the knowledge graph construction process.

This invention relates to the field of pest and disease early warning technology, and discloses a method and system for early warning of seed industry pests and diseases based on big data. The method includes acquiring environmental sound data and leaf image data; extracting lesions from the leaf image data to obtain lesion morphological features; extracting features from the environmental sound data to obtain pest sound features; matching the lesion morphological features and pest sound features with a pre-stored pest and disease database to obtain pest type and disease type; calculating the matching degree between the pest type and the disease type to obtain the pest-disease matching degree; and assessing the damage based on the pest-disease matching degree and a preset matching degree threshold to obtain the disease assessment result. This method has the following advantages: it can improve the accuracy of early warning of seed industry pests and diseases.

The invention relates to the technical field of track disease detection, and discloses a regional ballastless track disease detection method, device and equipment and a storage medium, and the method comprises the steps: generating a detection data uploading demand and detection data uploading capability of each detection sub-section according to a ballastless track historical disease database and a communication quality map; and then, detecting the vehicle speed as a core, and combining hardware constraint and a gear switching optimization target to solve an optimal detection strategy so as to guide execution of ballastless track detection driving, detection data uploading and cloud disease detection analysis. Therefore, by optimizing the detection speed of the ballastless track detection vehicle in different detection sub-sections, the problem that detection is stopped or old data is covered due to data accumulation in poor network areas such as tunnel groups and mountainous areas is thoroughly solved while it is ensured that detection is completed within the skylight period, the integrity of detection tasks and the reliability of data are guaranteed, and the detection efficiency is improved. The cloud is helped to quickly analyze and output disease detection results, and a timely basis is provided for track maintenance.

The invention relates to a miRNA-disease association prediction method based on trans-attention and confidence fusion, and the method comprises the steps: obtaining a miRNA-disease database, and constructing a heterogeneous graph; coding miRNA nodes and disease nodes in the heterogeneous graph to obtain miRNA and disease node embedded representation; according to the miRNA and the disease node embedded representation, introducing a trans-attention mechanism between the miRNA and the disease node to carry out bidirectional interaction, and obtaining an interaction result; constructing a plurality of channels, and obtaining miRNA-disease pair features and channel incidental confidence; and performing weighted fusion on the miRNA-disease pair features according to the channel incidental confidence, obtaining the fused miRNA-disease pair features, inputting the fused miRNA-disease pair features into a multi-layer perceptron, and outputting a probability value of association between miRNA and disease. According to the method, the feature extraction and prediction of the relationship between the miRNA and the disease can be realized, and confidence estimation is introduced in the prediction process to improve the reliability of the result.

The application provides a kind of nervous system closed loop control system based on transcranial photoacoustic cooperation, which is composed of EEG monitoring device, personal information, photoacoustic cooperation system, multi-modal large model and psychological disease database;For the transcranial infrared light and ultrasonic cooperative stimulation technology of patient, through optimizing stimulation parameters, realize the bidirectional regulation of photoacoustic, take multi-modal large model as the base, take multi-modal data fusion and reinforcement learning as the technical means, design closed loop photoacoustic cooperative stimulation system device and automatically optimize intervention parameters, realize the effective regulation method of psychological disease through non-invasive brain stimulation technology, realize the adaptive adjustment and closed loop feedback of parameters.Through real-time detection of patient's brain wave and collection of patient's personal information, evaluate the incoming system after integration, adjust intervention in real time according to different brain states, and use feedback to adjust the parameters of the next intervention and the precision regulation process of excitation.

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.