21 results about "Diseases database" patented technology

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 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 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 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.

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 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.

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 fetal congenital heart disease artificial intelligence multi-dimensional diagnosis system and a fetal congenital heart disease artificial intelligence multi-dimensional diagnosis method. The system comprises a multi-source heterogeneous special disease database module, a standard section intelligent identification module, a precise diagnosis and risk assessment module and an intelligent auxiliary decision making and cloud platform module. The method comprises the following steps: constructing a special disease database; automatically identifying a standard section in the ultrasonic video by using an AI model; carrying out segmentation quantization on the tangent plane; image features and clinical data are fused for multi-dimensional diagnosis; and generating a structured report. According to the method, the multi-modal data and the advanced AI algorithm are integrated, so that automatic and precise auxiliary diagnosis of the fetal congenital heart disease is realized, the missed diagnosis and misdiagnosis rate is effectively reduced, and the method is particularly suitable for improving the prenatal screening capability of primary medical institutions.

The application discloses a tunnel crack damage parameterization analysis method and device, wherein the method comprises the following steps: establishing a disease database by taking a tunnel section as a unit according to tunnel crack damage detection data; acquiring key characteristic parameters of the tunnel crack damage detection data to form a parameterization data sequence; setting different weights for each key characteristic parameter in the parameterization data sequence; determining the similarity between current tunnel crack damage detection data and historical tunnel crack detection data; matching the current tunnel crack damage detection data with the historical tunnel crack detection data according to the similarity, and classifying the current tunnel crack damage detection data according to a matching result; and evaluating the grade of the current tunnel crack damage detection data according to the key characteristic parameters of the current tunnel crack damage detection data and the classification result. The application can improve the processing efficiency of disease detection data, realize rapid classification of disease detection data, and evaluate the grade of tunnel crack damage.

The application relates to a miRNA-disease association prediction method based on cross-attention and confidence fusion, which comprises the following steps: acquiring a miRNA-disease database and constructing a heterogeneous graph; encoding miRNA nodes and disease nodes in the heterogeneous graph to obtain miRNA and disease node embedding representations; introducing a cross-attention mechanism between miRNA and disease nodes according to the miRNA and disease node embedding representations to realize bidirectional interaction and obtain an interaction result; constructing multiple channels to obtain miRNA-disease pair features and channel-attached confidence; weighting and fusing the miRNA-disease pair features according to the channel-attached confidence to obtain fused miRNA-disease pair features; inputting the fused miRNA-disease pair features into a multilayer perception machine to output probability values of the miRNA-disease association. The application can realize feature extraction and prediction of the relationship between miRNA and diseases, and can introduce confidence estimation in the prediction process to improve the reliability of the result.

A method for identifying a disease affected area. The method includes activating a geolocation device of an electronic communication device, determining a current geolocation from the geolocation device, querying a disease database from the electronic communication device, to identify one or more diseases associated with the current geolocation, and generating a graphical display on a display of the electronic communication device displaying a risk rating associated with each of the one or more identified diseases associated with the current geolocation.

The invention relates to a traditional Chinese medicine and western medicine combined intelligent complete-cycle comprehensive management platform for osteoporosis, and aims to solve the problems that screening is not systematic, diagnosis is not comprehensive, treatment individualization is insufficient, follow-up visit is not standard and data is dispersed in existing osteoporosis management. The platform comprises a data capturing module, a risk assessment module, an examination suggestion pushing module, a diagnosis integration module, a treatment scheme generation module, a follow-up visit management module and a data management module, and can be in butt joint with a hospital HIS system to capture patient data, grade patient risks through built-in IOF one-minute risk assessment questions, and push bone mineral density and laboratory examination suggestions; western medicine examination results and traditional Chinese medicine syndrome differentiation type clear diagnosis are integrated, and an individualized scheme containing basic treatment, anti-osteoporosis medicines, traditional Chinese medicines and exercise prescriptions is generated; the hospital discharge follow-up visit is realized by means of a WeChat applet and a Web terminal, and data of multiple hospitals are integrated to establish a special disease database. The whole-cycle intelligent management and control of osteoporosis are realized, and the diagnosis and treatment efficiency and standardization are improved.

We provide the system. [Solution] A means by which the user inputs symptoms into an input terminal, The server uses a natural language processing engine to analyze the input symptoms and extract keywords. A server searches a disease database based on extracted keywords and lists related diseases. The server provides a means to assess the risk level of listed diseases and also provides information on other related symptoms. A means by which the server recommends medical institutions capable of providing specialized treatment based on the user's location information, The server provides a means of offering general treatment guidelines for each disease, The means by which the terminal displays this information to the user, A system that includes this.