78 results about "Common disease" patented technology

The invention discloses a patient medication risk early warning method, and relates to the technical field of medication risk evaluation. The method comprises the following steps: acquiring multi-dimensional data such as age, past medical history, allergy history, clinical examination data and prescription data of a patient, extracting chronic disease diagnosis information from the multi-dimensional data, and carrying out standardized coding to generate a common disease combination tag; calling a preset disease weight system, and calculating a common disease severity score in combination with an age factor; extracting metabolism-related indexes to construct a drug metabolism capability feature vector of the patient; mapping the drug combination, the co-disease tag and the metabolic feature vector to a clinical drug knowledge base to generate a comprehensive conflict detection result; and generating corresponding early warning information based on the conflict detection result and a preset early warning rule base, wherein the rule base comprises a mapping relationship between the conflict type and the early warning level. According to the method, drug use risk early warning can be realized according to the multi-disease coexistence state of the patient, and the drug use safety is improved.

The invention provides an intelligent management system for chronic diseases and common diseases of old people. The intelligent management system comprises an old people comprehensive evaluation module, a multidisciplinary collaborative diagnosis and treatment module, a multi-medication risk prediction management module, an intelligent chronic disease follow-up diagnosis management module and a health science popularization knowledge base module, the senile comprehensive assessment module inputs the personal chronic disease map of the patient into a risk factor prediction model for prediction, and outputs a comprehensive assessment result; the multidisciplinary cooperative diagnosis and treatment module is used for the first diagnosis / receiving diagnosis doctor to coordinate multidisciplinary doctors to perform cooperative diagnosis and treatment; the multi-medication risk prediction management module is used for giving comprehensive diagnosis, multi-medication management and risk prediction to the patient through collaborative diagnosis and treatment; the chronic disease intelligent follow-up diagnosis management module carries out follow-up visit tracking, health risk prediction and review prompting on the patient; and the health science knowledge base module matches and pushes corresponding health education knowledge. The accuracy and timeliness of senile chronic disease co-disease risk assessment prediction and management are improved, and a good auxiliary effect is achieved on improvement of the life quality of patients.

The invention discloses a tropical pathogen spectrum multiple detection primer group based on a tNGS technology and application thereof. The multiple detection primer group comprises primers as shown in SEQ NO.: 1 to SEQ NO.: 260 in a sequence table. By utilizing the multiple detection primer group, 100 common pathogens in the tropical region can be efficiently and accurately detected in the same reaction system.

The invention discloses an artificial intelligence auxiliary medical decision-making system, and relates to the technical field of medical assistance. Through the detection suggestion module, preliminary reasoning and observation index judgment can be performed according to patient chief complaint in combination with basic information, and detection suggestions are given in combination with detection cost budget of a patient; through the diagnosis module, preliminary judgment can be made according to detection abnormity, disease classification is judged based on the current diagnosis result, further detection suggestions are given, and finally a doctor makes further diagnosis or treatment suggestions; through the treatment suggestion module, disease causes can be judged according to the diagnosis result, and detection cost budget of a patient is combined, so that the diagnosis and treatment efficiency is improved. Giving out treatment suggestions; the detection suggestion module, the diagnosis module and the treatment suggestion module jointly act to form a medical decision-making system, non-common diseases can be found easily, the problem of cross-department missed diagnosis is solved, the medical budget planning function is achieved, and doctors and patients can provide accurate treatment suggestions easily.

The invention belongs to the technical field of biological detection, and particularly discloses a targeted sequencing primer group and a kit for detecting common pathogens of children and drug-resistant genes thereof. The primer group comprises 145 primer pairs of common children pathogen primer groups and 67 primer pairs of drug-resistant gene primer groups of the common children pathogen primer groups. According to the kit provided by the invention, nucleic acid in a clinical sample is directly extracted, 212 pairs of specific primers are adopted to realize multi-targeted amplification of a pathogen target area, a high-throughput sequencing platform is adopted to accurately identify pathogens and drug-resistant mutant genes thereof, 72 common children pathogens and drug-resistant genes thereof can be detected at the same time, and the kit has a wide application prospect. More accurate diagnosis information is provided for clinic, a key technical support is provided for early-stage accurate treatment of children infectious diseases, and the technical problems that in the prior art, in children pathogen detection, the detection speed is low, the number of missed detection is large, and the drug-resistant blind area is large are solved.

The invention discloses a multi-intelligent body-assisted growth defect and rare disease intelligent analysis system based on a large language model, and relates to the technical field of artificial intelligence and medical treatment. The objective of the invention is to solve the problem of reasoning deviation of a large language model in medical diagnosis analysis. According to the system, a medical analysis process is decomposed into five steps of inquiry, potential common disease judgment, potential rare disease judgment, disease symptom query and comprehensive analysis, and the five steps are respectively and cooperatively completed by a chief complaint agent, a common disease agent, a rare disease agent, a disease background agent and an analysis agent. All agents are trained in a specific field, and through information interaction and cooperation, full-process processing from patient complaint to standardized medical language conversion, common / rare disease screening, disease background information generation and comprehensive analysis suggestion is achieved. Through intelligent agent division and cooperation, the accuracy and reliability of medical diagnosis analysis are remarkably improved, and the reasoning deviation of a single model is reduced.

The invention relates to the technical field of virus detection, in particular to a primer probe combination for bovine respiratory disease syndrome pathogen identification and application and a kit thereof. When the bovine viral diarrhea virus is identified, the nucleotide sequence of the upstream primer of the used primer probe group is as shown in SEQ ID No.1, the nucleotide sequence of the downstream primer is as shown in SEQ ID No.2, and the nucleotide sequence of the probe is as shown in SEQ ID No.3. The primer probe combination provided by the invention can be used for identifying and diagnosing eight common pathogens capable of causing the bovine respiratory disease syndrome at one time through a two-tube reaction system by using a fluorescent quantitative PCR method, can also be used for identifying a mixed infected sample, has the advantages of accurate identification, high sensitivity, simplicity in operation, short time consumption and the like, and can be used for detecting the bovine respiratory disease syndrome. And an effective technical means can be provided for pathogen diagnosis and disease prevention of the bovine respiratory disease syndrome in China.

The invention provides an intelligent health management method and system for chronic disease patients. The system comprises a physical sign abnormity module, a common disease association module, a common disease repulsion association module and a strategy module. The physical sign abnormity module can collect physical sign data, judge abnormal risk conditions of chronic diseases, and obtain abnormal physical sign data of the chronic diseases when judging that the chronic diseases have abnormal risks; the co-disease association module comprises associated diseases associated with chronic diseases, and the association degree of the chronic diseases with abnormal risks and the associated diseases is obtained; the co-disease exclusion association module comprises exclusion diseases with treatment counteracting effects with chronic diseases, and the chronic diseases with abnormal risks and the exclusion association degree of the exclusion diseases are obtained; and the strategy module allocates a corresponding common disease treatment strategy according to the association degree between the chronic disease with the abnormal risk and the associated disease and the exclusion association degree between the chronic disease and the exclusion associated disease. According to the invention, the system optimization is improved.

The invention discloses an intelligent hospital guide system, which belongs to the field of medical information and is characterized in that medical terms are converted into uncomfortable symptom description which is easy to recognize by a patient by deeply analyzing characteristics of common diseases and combining a natural language processing technology, so that the user friendliness of the system is improved; approximate Bayes inference is carried out by using cosine similarity, an improved C4.5 decision tree algorithm is combined, a decision tree is dynamically constructed to generate accurate question and answer logic, accurate matching and department recommendation of patient symptoms are realized, and the accuracy and efficiency of hospital guidance can be improved; in conclusion, the system provided by the invention effectively solves the defects of traditional manual hospital guide, improves the registration accuracy, and promotes the overall improvement of the medical service quality by reducing the hospital guide cost and improving the working efficiency.

The invention discloses a co-disease collaborative identification and risk early warning method based on multi-modal data and an adaptive large model. The method comprises the following steps: collecting multi-modal medical data; carrying out preprocessing and feature extraction on the multi-modal medical data; based on the fine-tuned adaptive large model, deep interactive fusion is carried out on the extracted multi-modal features, potential association among different data sources and mutual influence and synergistic effect among various diseases are learned, and combined judgment and future onset risk prediction of the current common disease state of the patient are output; determining a specific co-disease combination and a co-disease risk level based on the combined judgment of the current co-disease state of the patient and a future onset risk prediction result; and when the determined co-disease risk level is equal to or higher than an early warning threshold, generating a collaborative early warning and intervention suggestion. According to the invention, a cross-modal and cross-disease feature fusion technology can be utilized to realize conjoint analysis and comprehensive risk early warning of multiple disease states of the patient, and the co-disease management efficiency and the clinical auxiliary decision-making level are significantly improved.

The invention provides an old patient falling event data prediction method based on a common disease index. According to the method, health record data of a target elderly patient is obtained, a target associated disease data set is determined from the health record data according to a preset tumble associated disease map, a target co-disease index is determined according to the target associated disease data set, and then the tumble associated disease map is determined only when the target co-disease index is smaller than a preset index threshold value. And extracting a patient feature set according to the health record data, and inputting the patient feature set into a preset tumble event prediction model to output a tumble risk level of the target elderly patient, thereby realizing automatic prediction and evaluation of the tumble risk level of the target elderly patient.

The invention discloses a multi-strategy large language model common disease prediction method based on heuristic global perception, and the method comprises the steps: selecting four classical graph theory evaluation indexes, analyzing the index values of all edges in a disease network, and setting the value ranges corresponding to an average value, a median, an upper quartile and a lower quartile as threshold values; and constructing a 12-dimensional binary value feature vector of each edge based on the threshold value as a global feature, inputting the global feature into an SGD selector for training, and fixing parameters. And then, by taking a global graph theory feature prediction result as guidance, adopting a large language model RAG framework and combining with multi-strategy literature retrieval, and realizing interpretable output through an improved COT cue word. And performing dynamic selection on prediction results of global and local features through a linear regression model by adopting a supervised training mode. A large number of experiments verify the effectiveness of the method: the performance of the LLMs in a disease link prediction task is significantly improved by fusing the global and local features of the prediction edge.

The invention aims to provide the method for detecting, preventing and treating common diseases of the pomfret in the industrial aquaculture mode, so that the problem that the pomfret is easily infected by parasites, bacteria and viruses in a high-density aquaculture environment to cause large-scale death is solved, and the survival rate of artificially cultured pomfret is increased. According to the method, the disease condition of the bred pomfret can be simply and efficiently detected in a time-saving and labor-saving mode, a large number of experimental equipment is not needed, and operation can be conducted on the first line of pomfret breeding. Disease conditions can be found in the first time, control measures are taken in time, and the breeding survival rate is increased.

The invention belongs to the technical field of biology, and discloses a TaqMan primer probe combination for detecting virulent and attenuated Newcastle disease, which comprises a virulent primer RT, a virulent probe RT-probe, a universal primer SRT and a universal probe SRT-probe, the virulent primer RT comprises an upstream primer RT-F and a downstream primer RT-R, and the universal primer SRT comprises an upstream primer SRT-F and a downstream primer SRT-R; a TaqMan probe fluorescent quantitative RT-PCR (Reverse Transcription-Polymerase Chain Reaction) detection method for virulent and attenuated NDV (Newcastle Disease Virus) established by using the TaqMan primer probe combination has no cross reaction on amplification of common avian viruses AIV, FADV, IBV and IBDV and has good specificity; the lowest detection concentration of virulent and attenuated Newcastle disease virus is 101 copies / uL, which is 100 times higher than that of a conventional RT-PCR method, and the kit has good sensitivity; and compared with the conventional RT-PCR method, the detection rate is 100% and is superior to that of the conventional RT-PCR method, and the detection result is consistent with the sequencing result.

The invention belongs to the technical field of molecular detection, and particularly relates to a respiratory tract pathogen detection kit based on capillary electrophoresis and application of the respiratory tract pathogen detection kit, the kit can detect common pathogens which are possibly infected by respiratory tract patients and cannot be detected and determined through a conventional technology or a kit, and the kit at least has the technical advantages of being high in sensitivity and specificity.

The invention relates to the technical field of molecular biology. The invention provides a primer probe combination for detecting an H10 subtype avian influenza virus, the primer probe combination comprises a primer pair and a probe for detecting the H10 subtype avian influenza virus, and the primer pair for detecting the H10 subtype avian influenza virus comprises an H10-F sequence as shown in SEQ ID NO.1 and an H10-R sequence as shown in SEQ ID NO.2; and the probe for detecting the H10 subtype avian influenza virus is an H10-P sequence as shown in SEQ ID NO.3. The invention further discloses a kit for detecting the H10 subtype avian influenza virus. The primer probe combination disclosed by the invention can be used for detecting and distinguishing epidemic H10 viruses of European subpedigree and North American pedigree, does not generate cross reaction with other common viruses, and is high in detection sensitivity, and the minimum detectable quantity is 5 copies.

The invention provides an intelligent evaluation method and an intelligent evaluation system for community common-disease home medication, which are suitable for monitoring the home medication safety of old people with common diseases. The method comprises the following steps: constructing a dynamic knowledge graph including disease combination nodes, drug information nodes, individual feature nodes and association relationships thereof; long-term medication data and temporary medication data of a user are obtained through a multi-mode interaction interface integrating optical character recognition, voice recognition and natural language processing technologies; performing triple conflict analysis on the medication data based on the dynamic knowledge graph, wherein the triple conflict analysis comprises drug interaction prediction, disease taboo matching and individual feature cross validation; and generating high, medium and low drug use risk levels according to a conflict analysis result, and triggering an emergency notification protocol when the risk is high.

The invention relates to the technical field of biological detection, relates to a dual RT-ddPCR detection method for equine encephalomyelitis viruses in the eastern and western parts, and in particular relates to a monitoring and quantitative detection method for samples with relatively low equine encephalomyelitis virus content or complex matrix, such as feeds, animal tissues and animal-derived products. The double reverse transcription microdroplet digital PCR detection method for the equine encephalomyelitis virus, established by the invention, is strong in specificity, high in sensitivity, good in repeatability and strong in anti-interference capability, and can be used for absolutely quantifying the copy concentration of the eastern equine encephalomyelitis virus EEEV and the western equine encephalomyelitis virus WEEV in an actual sample; a novel and reliable technical method is provided for early trace early warning and monitoring of Eastern equine encephalomyelitis virus EEEV and Western equine encephalomyelitis virus WEEV infection, differential diagnosis of other common viruses, research of propagation rules of the viruses, epidemiological risk assessment and other systematic applications.

The invention discloses a key acupuncture point node mining method based on an acupuncture point-disease network, and belongs to the crossing field of information systems and empirical medicine. Comprising the steps of establishing a weighted undirected acupoint-disease network based on a clinical acupuncture prescription, providing a high-specificity key node mining method based on the acupoint-disease network, and balancing contribution of a local area to the network on the basis of meridian distribution characteristics in combination with a traditional Chinese medicine acupoint selection principle. And key acupoints with high influence on global topology are selected according to the evaluation indexes of the key nodes. According to the method, key acupuncture points of meridians and collaterals in each region of a human body are positioned and mined by using a complex network theory in the field of an information system, and the key acupuncture points which have obvious influence on various common diseases of the human body are positioned from a new perspective, so that other key acupuncture points in local meridians and collaterals of the human body are determined; and an empirical medical researcher is assisted to find the optimal prescription acupoint for a specific disease under the resource constraint.

The invention relates to the technical field of biological detection, in particular to a primer probe group, a detection reagent and a detection kit for detecting glycogen synthase kinase 3 genes. The primer probe group of the GSK-3beta gene provided by the invention is high in specificity, the amplification performance of the detection reagent is excellent, the kit provided by the invention has the advantages of high specificity, high sensitivity and high accuracy when being used for rapidly identifying GSK-3beta in a kidney lesion tissue sample or urine, the stable detection lower limit is 0.05 ng / mu L, and the kit can be used for rapidly identifying the GSK-3beta in the kidney lesion tissue sample or urine. In addition, interference of other types of common pathogens on experimental results can be avoided, and a reliable experimental basis is provided for GSK-3beta identification.

The present invention relates to the field of emergency management technology, and specifically discloses a method and system for optimizing a disease repair strategy for improving the resilience of a rail transit network. The method includes forwarding a disease reporting request to a main terminal upon receipt, receiving a disease treatment plan sent by the main terminal, and calculating network performance parameters of the disease treatment plan; clustering the disease reporting requests to obtain each type of disease reporting request; comparing the network performance parameters of the disease treatment plan corresponding to each disease reporting request in the same type of disease reporting request, and selecting the optimal plan; and when a new disease reporting request is received, querying the optimal plan, serving as a reference plan, and feeding it back to the main terminal. The present invention establishes a weighted interval capacity evaluation index by integrating the network topology characteristics and actual operation characteristics of the rail transit line infrastructure, and combining the corresponding maintenance processes and work efficiency of common diseases, with excellent evaluation results.

The application discloses a parrot-origin reovirus RT-qPCR specific detection primer and a detection method. The primer probe composition takes a S1 gene conservative region of the parrot-origin reovirus as an amplification target, and comprises a specific upstream primer, a downstream primer and a fluorescent probe. The nucleotide sequence of the upstream primer is shown as SEQ ID NO:1, the nucleotide sequence of the downstream primer PSRV-S1-R is shown as SEQ ID NO:2, and the nucleotide sequence of the fluorescent probe PSRV-S1-Probe is shown as SEQ ID NO:3. The application designs specific primers and a TaqMan probe for the parrot-origin reovirus, has an amplification signal for the PSRV, has no cross reaction with common pathogens such as APV, BFDV, FAdV, ARV, NDRV and parrot fever chlamydia, has good specificity, is accurate in quantification, has good repeatability and stability, and is simple in operation and high in flux.

The present invention adopts the RPA combined with the LFS technology to establish a rapid and sensitive on-site detection method for Streptococcus pneumoniae. This method designs primers and probes based on the autolysin gene (lytA) of Streptococcus pneumoniae and completes the detection within 30 minutes at 37°C. By detecting 22 clinical isolates of Streptococcus pneumoniae and 20 other common pathogenic bacteria, the specificity of this method was verified. Ten independent experiments were carried out on the sensitivity of the RPA-LFS method, and the lowest detection limit was 3.32 colony forming units (CFU) / reaction. Finally, the established RPA-LFS detection method for Streptococcus pneumoniae was used to detect clinical specimens. Compared with the PCR method, the detection results were accurate and consistent. In summary, this study developed a rapid, sensitive and specific RPA-LFS detection method for Streptococcus pneumoniae, which has good application prospects in primary medical diagnosis in remote and resource-limited areas.

The invention discloses a respiratory tract pathogen PCR-HRM detection kit and application thereof, and belongs to the technical field of detection, and the kit comprises primer probes as shown in SEQ ID No.1-90. Based on a PCR-HRM (Polymerase Chain Reaction-High Resolution Melting) method, the detection kit for detecting the 26 common pathogens of the respiratory tract syndrome, established by the invention, has the detection sensitivity of 100 Copies / mL (preferably 50 Copies / mL), and can meet the expected requirement of monitoring the pathogens at a port. In the correctness verification process, compared with a tNGS detection result, the conformity of the detected target species is not lower than 96.1%, which indicates that the method has high reliability and accuracy in pathogen detection.

The invention relates to the technical field of emergency management, and particularly discloses a disease repair strategy optimization method and system for improving the toughness of a rail transit network, and the method comprises the steps: receiving a disease report request, forwarding the request to a total terminal, receiving a disease processing scheme sent by the total terminal, and calculating the network performance parameters of the disease processing scheme; clustering the disease reporting requests to obtain each type of disease reporting requests; comparing the network performance parameters of the disease processing schemes corresponding to the disease reporting requests in the same kind of disease reporting requests, and selecting an optimal scheme; and when a new disease report request is received, querying the optimal scheme as a reference scheme, and feeding back the reference scheme to the general end. According to the method, the network topology characteristics and the actual operation characteristics of the infrastructure of the rail transit line are fused, the maintenance process and the work efficiency corresponding to common diseases are combined, the weighted interval trafficability evaluation index is established, and the evaluation effect is excellent.

The application discloses a disease recognition method and device based on multi-modal fusion, comprising the following steps: multi-modal feature extraction processing is performed on multi-modal original data, and quality and confidence of the multi-modal features are evaluated; based on an attention mechanism, dynamic weight generation processing is performed on the quality and confidence evaluation results of the multi-modal features, and adaptive feature fusion is performed on the features of each mode; first-stage preliminary screening processing is performed on the adaptive feature fusion results, and a heat map of an unusual case abnormal area or a common case diagnosis result is output; second-stage refined diagnosis is performed on the first-stage preliminary screening processing results in combination with a medical knowledge graph, and a refined diagnosis report is generated; in the first diagnosis stage, rapid screening positioning is realized, and in the second diagnosis stage, suspicious problems are analyzed in depth, so that it is explained which data are used for diagnosis, where the screening positioning result of the first diagnosis stage is observed, and what the second diagnosis stage identification reason is.

Described herein are methods and compositions related to small variant calling Characterizing rare variants implicated in common diseases remains a challenge. Towards these aims, computational efficiency of variant calling have leveraged more advanced computational techniques, including to improve variation detection across more samples or and meet quality control standards for variant calls. Nevertheless, there remains a great need in the art for faster, more effective and accurate variant detection. Here, a small variant calling model based on an error-rate is provided.

The application discloses a MIRA-LFD primer probe composition for visual rapid detection of DHAV-1 and DHAV-3, a kit and application thereof. The primer probe composition comprises a first primer pair and a probe for detecting DHAV-1, and a second primer pair and a probe for detecting DHAV-3. The application is based on a multi-enzyme constant temperature rapid amplification technology combined with a lateral flow chromatographic test strip, and visual double detection of DHAV-1 and DHAV-3 can be completed within 15 min under a constant temperature condition of 39 DEG C, and the minimum detection limit is 1.5 x 10 2 copies / muL, and there is no cross reaction with other common viruses of duck origin. The primer probe composition and the kit are used for detecting clinical samples, and the result is consistent with that of an RT-PCR method, and the primer probe composition and the kit have the advantages of simple operation, rapidness, and no need of expensive instruments.

The invention relates to the technical field of molecular biological detection. The invention provides a primer pair and a probe for detecting chlamydia abortus, and further provides a kit which comprises the primer pair and the probe and is used for quantitatively detecting the chlamydia abortus through micro-droplet digital PCR, and a non-diagnostic method for quantitatively detecting the chlamydia abortus based on the micro-droplet digital PCR. And negative and positive results are judged according to the fluorescence signal condition of the microdroplet in the amplification product. According to the technical scheme, rapid and effective identification of the chlamydia abortus disease can be achieved, cross reaction with other common pathogens can be avoided while the chlamydia abortus is detected, and the method has good specificity, good repeatability and high sensitivity.

The invention discloses a knowledge graph-based intelligent auxiliary diagnosis and treatment system for grassroots medical common diseases, and relates to the technical field of medical care informatics, and the system comprises the following steps: 1, obtaining a medical record text of a patient, and taking a patient element anchoring node set as a starting point to execute subgraph traversal to extract candidate diagnosis and treatment subgraphs; 2, extracting patient observation facts and diagnosis knowledge facts from the candidate diagnosis and treatment subgraphs to form a diagnosis logic fact set, and arranging the diagnosis logic fact set in a descending order according to comprehensive evidence intensity to form candidate disease reasoning results; and step 3, coding clinical diagnosis and treatment path specifications into a fuzzy Petri network, and summarizing to generate an intelligent auxiliary diagnosis and treatment result. According to the method, the interpretability of logical reasoning, the fuzzy matching capability of semantic embedding and the differentiated path constraint of the fuzzy Petri network are organically fused, and a standardized and interpretable intelligent auxiliary diagnosis and treatment result can be provided under the condition that basic medical diagnosis evidence is incomplete and sufficient.