91 results about "Diseases database" patented technology

A system is provided for collecting and storing immunization and disease data. Immunization mobile stations (IMSs) are provided with a software application to facilitate the collection of patient information such as biographical data, previous vaccination data, medical history, medications in use, occupation, administration of recent vaccination, disease symptoms and the like. IMSs synchronize the patient information with information stored in a database maintained at a data center that is accessible to different groups of personnel based on different privileges defined at the data center and security measures. Patients can access electronic patient records created by the IMSs, and stored at the IMSs and / or a local server and eventually at the data center, via telephone or computer (e.g., via web browser). IMSs can capture and store images of vaccination and disease symptom sites on patients. The database allows for vaccination and disease tracking and disease control. The IMSs can be provided with a vaccination recommendation engine to determine if a patient is eligible for a vaccination and electronic patient consent forms, and are programmable to track adverse events and create follow-up reports after a vaccine is administered.

The invention relates to a smart agricultural insect disease remote automatic diagnosis system. The system comprises an interaction system, an identification system and an insect disease database management system. The interaction system comprises an information acquisition system, a wireless communication system and an information display system; the identification system comprises a similarity comparison system and a remarkable feature extraction system; and the inset disease database management system comprises an insect disease picture database, an insect disease remarkable feature database and an insect disease diagnosis database. The smart agricultural insect disease remote automatic diagnosis system, based on images of crop growth conditions, through intersection operation of the image similarity and remarkable feature extraction comparison system and a machine learning algorithm of the identification system, can effectively improve the identification accuracy of an insect disease type. According to the insect disease type, the system provides an omni-directional diagnosis scheme so as to improve the survival rate and the quality of crops.

A system is provided for collecting and storing immunization and disease data. Immunization mobile stations (IMSs) are provided with a software application to facilitate the collection of patient information such as biographical data, previous vaccination data, medical history, medications in use, occupation, administration of recent vaccination, disease symptoms and the like. IMSs synchronize the patient information with information stored in a database maintained at a data center that is accessible to different groups of personnel based on different privileges defined at the data center and security measures. Patients can access electronic patient records created by the IMSs, and stored at the IMSs and / or a local server and eventually at the data center, via telephone or computer (e.g., via web browser). IMSs can capture and store images of vaccination and disease symptom sites on patients. The database allows for vaccination and disease tracking and disease control. The IMSs can be provided with a vaccination recommendation engine to determine if a patient is eligible for a vaccination and electronic patient consent forms, and are programmable to track adverse events and create follow-up reports after a vaccine is administered.

The invention provides a hospital decision-making aiding method based on symptom similarity analysis, belonging to the field of information analysis and decision-making aid. The method comprises the following steps: establishing a disease database, a symptom database, a department database and a correlating relationship; establishing a patient model for symptoms input by users, and calculating the similarity between the patient model and a disease model; and searching one department with corresponding suspected diseases in the department model to serve as hospital reference. The method has the advantages that an error interrupted result is weakened under a condition that a user takes an error symptom as a reasoning condition; a correct symptom participates in the reasoning operation by combining the correlating relationship between the error symptom and the convention symptoms according to the error symptom condition so as to realize the correctness and interruption resistance of reasoning; and a hospital department list is obtained by utilizing the mapping relationship between the disease and the department. The method can be used in electronic reception or health consulting systems, as well as in medical training or clinical reference.

The invention discloses a department triage system based on diagnostic element analysis, belonging to the field of medical information processing and network application. The department triage system comprises a logic calling management and service interface, a suspected disease reasoning module, a department reasoning module, a knowledge node positioning module, a knowledge node association module, a user guide module and the like. According to the symptoms provided by a user, diseases associated with the symptoms are searched in a predetermined disease database and ranked from high to low according to relevancy or suspecting probability; and visiting department analysis is conducted according to the suspected disease selected by the user. The triage and distribution pressure of hospitals is effectively relieved, people are prevented from seeing a doctor blindly, especially, patients suffering from infectious diseases are prevented from seeing a doctor blindly and staying at public area, pathophoresis and cross infection are avoided; and the public are enabled to clearly know which department to go to under the condition of preliminarily knowing about the probability of falling ill, and the informatization degree of triage consultation operation is improved.

The invention discloses an on-line insurance recommendation method and an on-line insurance recommendation system. According to a user file database, a user clinic disease database, a user clinic doctor record database, an insurance product database and the like and function controls associated with the databases, the system is used for recommending an insurance product to a patient after automatic matching of a platform based on file information, a clinic disease subject and clinic doctor record; and the premium amount and income of the recommended insurance product can be calculated on line. By the on-line insurance recommendation system, an insurance company can provide diverse and personalized insurance product service which is in accordance with user condition, the publicity and the awareness of insurance products which meet user demand are improved, and the insurance volume of the insurance product is further increased. Compared with the network sale of the insurance products, the invention has the advantages that: under the condition of fixed advertisement space size, the sale volume of the insurance product is increased, and the on-line sale of the insurance product on a network is facilitated.

The invention provides a drug pushing method and system. The drug pushing method comprises the following steps: obtaining a disease symptom of a user; obtaining a disease diagnosis result corresponding to the disease symptom through a disease database; and determining recommended drugs and medication instructions corresponding to the disease diagnosis result. Therefore, the user can obtain relatively professional and accurate disease diagnosis and medication guidance under the condition of not going to the hospital; time and effort in the seek treatment process are reduced; the medication suggestion accuracy is improved; the drugs selected by the user correspond to the disease symptom; and the disease recovery speed is increased.

An automated system for use by pharmacies, clinics or health care institutions which provides health information that comprises disease state information, the system having:a) a clinic computer having access to medical information containing a medication profile for the patient, andb) a remote computer having access to a drug-disease databaseThe disease state information being determined by a process where the clinic computer analyzes the medication information, creating a data packet comprising drug products being used, associating a unique identification number (UIDN) to the patient and data packet, and transmitting the UIDN and data packet to the remote computer. The remote computer then cross-referencing the drug products contained within said data packet with individual disease states and identifying disease state information that is relevant to be provided to a patient.

A remote healthcare method is applied to a healthcare server and includes the steps of receiving detected information from a physiological parameter detecting instrument, reading a patient identification code and a physiological parameter from the detected information, reading an identification datum and a medical history datum matched with the patient identification code from a history database according to the identification code, reading a disease matched with the disease classification code and a corresponding pathological symptom from the disease database according to at least one disease classification code in the medical history data, screening a corresponding matched pathological symptom from the pathological symptoms according to the read physiological parameter to create a self-perceived list, transmitting the self-perceived list to the physiological parameter detecting instrument, and waiting for a receipt of the selected option of the pathological symptom for determining an appropriate medical dosage or medical tool.

The invention discloses a skin disease comprehensive data analysis and diagnosis auxiliary system and an information processing method thereof. The skin disease comprehensive data analysis and diagnosis auxiliary system comprises an information extraction system; a patient information database; an electronic medical record learning system; a medical history-disease database; an image database; a skin iconography image feature recognition and analysis system; a feature-disease database; and an expert database. According to the invention, artificial intelligence is used to analyze skin iconography image data containing a lot of skin disease characteristics, in combination with double diagnosis results of clinical electronic medical record text data, comprehensive skin imaging pictures and electronic medical records, the skin disease comprehensive data analysis and diagnosis auxiliary system is provided, the system can automatically generate and feed back a target disease range, and moreobjective and reliable diagnosis and treatment suggestions are given compared with an existing artificial intelligence diagnosis system. The information processing method of the skin disease comprehensive data analysis and diagnosis auxiliary system is a method for processing the skin iconography picture data information when the system runs.

The present invention discloses a sound-based body condition determination system and method. The system comprises a sound collecting module for collecting sound information of a people to be tested,a noise reducing module for performing noise reduction processing on the sound information to obtain a noise reduction sound, an analysis module for analyzing noise reduction sound information to obtain a sound characteristic, an integration module for integrating the sound characteristic, environment data and sign data into a sound data set, a database establishing module for establishing a people-to-be-tested database and a disease database, a first judging module for judging the similarity between a sound data group and a sound data group of the people-to-be-tested database, and a second judging module for judging the similarity between the sound data group and a sound data group of the disease database so as to determine the type of a disease of the people-to-be-tested. By using the method and the system provided by the invention, the remote non-contact intelligent diagnosis is realized, and the probability of misdiagnosis is reduced with the assistance of existing diagnosis means.

The invention discloses a medical data processing method and system. The method includes the following steps: training data is received, a deep learning model is loaded, the training data is input tothe deep learning model, a training model is obtained through a transfer learning method and saved to a server, and a training model list L stored in the server is sent to a client; the client loads the training model, collects a medical image, and uses the training model to identify the medical image to get an identification result R; and the client sends the identification result R to the server, and the server retrieves a disease corresponding to the identification result R in a disease database according to the identification result R and gets a final auxiliary diagnosis result Y. The deeplearning method in artificial intelligence is adopted, and the processing precision of medical data is improved through artificial intelligence.

The invention relates to a method for detecting chromosome microdeletion and micro-duplication of a human embryo. The method comprises the following steps of performing whole genome amplification on cells cultured in vitro, interrupting DNA (deoxyribonucleic acid) molecules, and sequencing DNA fragments to obtain sequencing reads; comparing the sequencing reads with a reference sequence, and positioning the sequencing reads on the reference sequence; screening non-repeated areas of the reference sequence, and reserving the non-repeated areas; establishing a matrix of read number in windows through normal samples, analyzing the data of the normal samples, performing statistics on the read number of all the windows in the non-repeated areas, and establishing a probability matrix of the read number and chromosome enpeoids; calculating the copy number, i.e., the A / B / C state, of loci; selecting m continuous loci, i.e., the A state, as micro-duplication loci, and selecting m continuous loci, i.e., the C state, as microdeletion loci; contrasting the micro-duplication loci and the microdeletion loci with the existing CNV (copy number variation) and disease database, performing basic gene annotation and gene function analysis which relates to deletion parts, and annotating with a microdeletion syndrome disease type.

The invention provides a ballistocardiogram signal atrial fibrillation computer-aided diagnosis method based on transfer learning, wherein the method relates to the field of computer-aided diagnosis technology. The method comprises the steps of preprocessing an ECG signal and a BCG signal which are synchronously measured and the ECG signal in a current atrial fibrillation disease database, and setting the subframe length of all signal data to 24 seconds; then constructing a convolutional neural network which performs atrial fibrillation aided detection; training the constructed convolutional neural network and performing parameter updating; extracting a transfer learning characteristic vector of the actual measured BCG signal and constructing a random forest classifier; using the BCG signal in a testing set as the input of the convolutional neural network, acquiring a transfer learning model characteristic parameter and inputting into the random forest classifier, and determining whether the BCG signal is an atrial fibrillation disease type through the output result of the random classifier. The method of the invention facilitates extension of the BCG signal to a daily applicationfield and supplies a feasible plan for performing heart-related disease aided diagnosis and prediction through the BCG.

Feature of a compound is predicted by using information on interactions between substances. A database of interactions between compounds and genes / proteins is constructed on the base of information collected from bibliographic databases, gene / protein databases, and disease databases, and an interaction network is prepared by mapping the collected information to thereby enable prediction of the features of a compound.

The invention provides a clinical data automatic extracting and calculating method, system and device and a storage medium. The method comprises the following steps of: establishing a scientific research project queue on a special disease database, wherein clinical data of a special patient is stored in the special disease database; screening patients by adopting a nano-grid mode to generate scientific research project data, wherein the scientific research project data comprises screened clinical data of the patients; establishing an extraction rule and performing filtering and / or aggregationcalculation on the scientific research project data through the extraction rule to generate baseline data for the scientific research project, wherein the baseline data for the scientific research project comprises scientific research target fields required by a plurality of scientific research projects, and finally deriving the baseline data for the scientific research project. Under a conditionof an existing objective clinical full data set, according to the technical scheme, by the adoption of the clinical data automatic extracting and calculating method, system and device and the storagemedium, automatic extraction and calculation of the specific data required by CRF for clinical test or research are achieved, and the efficiency and accuracy of specific clinical data acquisition areimproved.

The invention is applicable to the technical field of genetic variation detection, the invention provides a method and a device and terminal equipment for detecting genetic variation of a hereditary disease; the method comprises the following steps: acquiring gene sequencing data of a hereditary disease sample; carrying out re-sequencing data analysis on the gene sequencing data, and detecting mutation sites in the gene sequencing data; performing mutation annotation on the mutation sites according to a preset disease database to obtain annotation results of the mutation sites; and determininga mutation detection result of the mutation sites according to the annotation results of the mutation sites. The method disclosed by the invention can be used for accurately judging hereditary disease-related genes, false positive sites and false negative sites are reduced, and the accuracy of gene variation detection is improved.

An image segmentation method and system for a pavement disease based on deep learning are provided, relating to a field of image processing. The image segmentation method includes steps of: acquiring a pavement detection image; inputting the pavement detection image into a disease segmentation model which is obtained through training a deep learning network with a disease database; recognizing and segmenting the pavement disease, and obtaining a segmented image of the pavement disease. The image segmentation method adopts a deep learning algorithm for image segmentation, so that a pavement disease region is automatically obtained, a working efficiency is improved and meanwhile image segmentation becomes more accurate.

The present disclosure relates to a data fusion method and apparatus for a medical database, an electronic device and a computer readable medium. The invention relates to the field of medical information processing. The method comprises the following steps: determining the data to be processed in a database according to the medical data to be fused, wherein the database is an elasticsearch database; determining a packet and a table to be processed according to the data to be processed; fusing the medical data in the database by the grouping and the table; and updating an index of the databaseto provide a real-time database query. The disclosed data fusion method and device, an electronic device and a computer-readable medium of medical database can reflux medical follow-up and verification data into disease-based database in real time and online.

The invention discloses a pavement disease recognition, management and maintenance decision making method and system based on internet big data. The method comprises the following steps that 1, a pavement disease database is constructed based on the internet big data, key sentences for determining and recognizing different pavement diseases are extracted from the pavement disease database, and user input standards are formulated according to the key sentences; 2, according to the user standards, pavement disease characteristics are input, searching and matching are performed in the pavement disease database through the data mining technology, intelligent recognition of pavement diseases is performed, and a management and maintenance scheme is given; 3, the pavement disease database performs self-learning through the user input data when being used by a user, self-upgrading is performed continuously to improve the recognition precision. The recognition accuracy and the maintenance scheme scientificity are improved, secondary pavement diseases caused by error diagnosis are avoided, and the maintenance cost is greatly reduced; the basic subject and the internet technology are combined, so that traditional engineering experience pavement disease recognition is turned into informatization and intelligentization.

The invention relates to image processing, in particular to a pavement disease image segmentation method and system based on deep learning. The pavement disease image segmentation method based on deeplearning comprises the steps of obtaining a pavement detection image; inputting the pavement detection image into a disease segmentation model obtained by training by using a disease database througha deep learning network; and recognizing and segmenting the pavement diseases to obtain a pavement disease segmentation image. According to the disease segmentation method provided by the invention,image segmentation is carried out by adopting a deep learning algorithm, automatic acquisition of a pavement disease area is realized, and the image segmentation is more accurate while the working efficiency is improved.

The invention is suitable for the technical field of medical information and provides a rapid intraoperative pathological diagnosis system and method. The system comprises a sample preparation module, an optical imaging module and a data analysis module, wherein the sample preparation module extracts a diseased sample tissue from a patient body, fixes the diseased sample tissue by adopting a chemical reagent, then performs optical clearing treatment on the diseased sample tissue by adopting an optical clearing agent and finally performs fluorescent staining treatment on the diseased sample tissue; the optical imaging module first arranges and fixes the diseased sample tissue subjected to the fluorescent staining treatment, and then performs optical scanning imaging treatment on the diseased sample tissue subjected to the fluorescent staining treatment, by adopting a functional microimaging system, so as to obtain a functional diagram and an optical section diagram of the diseased sample tissue; and the data analysis module performs comparative analysis on the functional diagram and the optical section diagram as well as existing disease database data to generate a diagnosis result report. The system and the method are high in section-making speed and high in section-making quality.

The invention relates to a health data analysis report generating system and method based on gene sequence analysis. The system comprises a diseases database module, a manual annotation module, a dataacquisition module, a data analysis module, a data storage module, a template library and a report generation module, wherein the data acquisition module, the data analysis module, the data storage module, the template library and the report generation module are sequentially connected, the diseases database module is connected with the template library, and the manual annotation module is connected with report generation module. According to the system, a health analysis report is generated by the aid of a mode combining an automatic retrieval diseases database with a manual annotation supplementary information, contents are automatically, conveniently and rapidly searched by a computer, labor is saved, a health analysis report generating link is simplified, Chinese version of gene annotation databases is absent at present, a computer system cannot automatically capture data, the contents of the report can be more all-round by the aid of manual annotation, understanding of readers isfacilitated, and practical application requirements are met.

The invention discloses a resource allocation method and system based on a block chain application. The method comprises an encryption step, a block data retrieval step, an intelligent contract generation step, a message channel transmission step, a user authentication step and an identity authorization step. The system comprises an encryption unit, a block data retrieval unit, an intelligent contract generation unit, a message channel transmission unit, a user authentication unit and an identity authorization unit, and is used for realizing the resource allocation method. The method and system are used for resource allocation of rescue work for patients with severe mental disorder. A blockchain, an intelligent contract and a cryptography technology are used to establish a resource allocation block chain application on the basis of a severe mental disorder patient service platform, a characteristic function block service is formed, and an effective work network and an effective supervision network are connected in parallel to establish a unified, centralized and real-time psychiatric disorder disease database, thereby enhancing the management of the rescue work of patients with severe mental disorder, and promoting the construction and utilization of a data sharing platform.

The invention relates to methods for diagnosing mulötiple sclerosis with miRNA markers. Diagnosis of multiple sclerosis (MS) can be challenging in patients with atypical presentations and during a first neurological deficit possibly related to inflammatory demyelination. Towards the identification of biomarkers for diagnosis of MS, a comprehensive analysis of miRNA expression patterns was obtained. Significantly deregulated miRNAs were identified, which have previously not been related to MS according to the microRNA disease database. These miRNAs could potentially serve as future diagnostic biomarkers for MS and help in diagnosis, monitoring disease activity, and evaluation of treatment responses in patients with MS.

The invention discloses a bituminous pavement rainy season minor repair early warning system and a bituminous pavement rainy season minor repair early warning method. The method includes the following steps that: bituminous pavement diseases are recognized and are put into statistics, so that a pavement disease database can be formed; big data collection is carried out, so that a rainy season pothole repair rate prediction model is established; a rainy season minor repair (pothole repair) early-warning system is established; and a bituminous pavement rainy season minor repair index is issued through a mobile terminal before the rainy season comes, so that rainy season minor repair work of a highway management unit can be guided. According to the bituminous pavement rainy season minor repair early warning method adopting big data analysis, data collection and analysis are carried out for formation reasons of bituminous pavement potholes in the rainy season; the rainy season pothole repair rate prediction model according with engineering practical experiences is established; the bituminous pavement rainy season minor repair index is issued through the rainy season minor repair early warning system before the rainy season comes; and therefore, prediction and early warning can be carried out for the rainy season minor repair work of the highway management unit, and a platform basis is provided for pre-event management.

The invention discloses an auxiliary prescription system based on medical big data, and the system builds a disease database and a prescription database based on the medical big data, i.e., the system can query a disease matching word matched with the disease information in the disease database according to the disease information of a user, and finally carries out the query of the disease matching word in the prescription database. On the basis of the disease matching word and the age and gender of the user, a medical prescription most matched with the disease information can be searched in a prescription database; therefore, objective medication guidance can be provided for medical workers for the medication scheme of the patient, the probability of medical negligence caused by medication errors is reduced, meanwhile, remote treatment of the user can be facilitated for common diseases, the treatment time of the user is saved, medical resources are saved, and large-scale application and popularization are facilitated.

The invention discloses a miRNA-disease association identification method and system based on fusion attributes. The method comprises the following steps: firstly, calculating functional similarity between any two miRNAs in a disease database, constructing an miRNA network undirected graph according to the functional similarity, calculating an aggregation coefficient between any two different miRNAs, and fusing the functional similarity and the aggregation coefficient between the two miRNAs to obtain a combined weight; secondly, calculating the weight of each miRNA vertex according to the combined weight, performing descending sort on each miRNA according to the weight, and screening out potential miRNAs related to diseases according to a descending sort result. The method is simple to implement; on the basis of fusing two attributes of topology attributes and function similarity, the miRNA nodes are weighted and sequenced in a descending order, and then potential correlation between miRNA and diseases is predicted by means of a disease database related to human miRNA and by means of a sequenced result, so the miRNA-disease correlation identification accuracy is improved, and valuable clues can be provided for medical diagnosis.

A method of evaluating disease severity is disclosed. The method uses physiological parameters and a historical database of diseases to determine a disease presented by the physiological parameters, to evaluate the disease severity and developing tendency, and to determine a suggested handling order when multiple diseases occur at the same time. The method provides a training stage and an executing stage. The training stage includes obtaining historical disease data from the historical database of diseases, and establishing a most-suited math model for the disease. The executing stage includes normalizing physiological parameters, calculating a strength value of the disease, and calculating a priority value based on the strength value of the disease and a probability value of the disease being selected in order to determine the suggested handling order. A system of evaluating disease severity is also disclosed therein.