310 results about "Chronic disease" patented technology

The invention relates to the technical field of medical health monitoring, and particularly discloses a chronic disease patient physical examination data and complication nursing monitoring system based on the Internet of Things, multi-dimensional physical examination data such as vital signs and blood biochemical indexes of a patient are collected in real time through a multi-source sensing equipment network, and an individualized data stream is constructed; extracting parameter collaborative change characteristics by adopting multi-time scale analysis, and constructing a dynamic physiological characteristic spectrum; identifying the abnormal offset by calculating the deviation degree of the feature vector distribution and the reference distribution; based on intelligent matching of the spatial distribution features of the feature vectors and a clinical case library, generating structured early warning information including risk assessment and personalized nursing schemes; and finally, visual display and preventive nursing intervention of the complication risk evolution process are realized.

The invention relates to the technical field of medical artificial intelligence and chronic kidney disease management crossing, in particular to a nonlinear algorithm system for precise chronic disease management of chronic kidney diseases. Through deep coupling of algorithms and in combination with two exclusive quantification formulas, transformation of CKD chronic disease management from standardized follow-up visit to precise and dynamic algorithm driving is realized, and the core pain points of large staging evaluation deviation, progress pre-judgment lag, poor intervention scheme adaptability, follow-up visit strategy homogenization and the like in traditional CKD management are solved. The method provides full-cycle decision support for nephrology doctors in the three-A hospital, customizes an individualized chronic disease management path for CKD patients, is suitable for CKD 1-5 period full-staging patients, and can be widely applied to CKD special management scenes of chronic disease diagnosis and treatment centers in the nephrology department of the three-A hospital, chronic disease management outpatient clinics in special nephropathy hospitals and high-end health management institutions.

The invention relates to the technical field of chronic disease health management, and discloses a chronic disease health management method based on data analysis, and the method comprises the following steps: S1, obtaining medical records, wearable device data, laboratory results, self-reporting information and environment data of a patient through a multi-source collection gateway; and S2, performing desensitization processing and feature extraction on the data under a federated learning framework, and generating a multi-modal patient portrait containing time sequence, spatial features and biomarkers. According to the chronic disease health management method and system based on data analysis, through desensitization processing under a federated learning framework, on the premise of protecting patient data privacy, medical records, wearable device data, environment data and other multi-source information are effectively integrated, a comprehensive multi-modal patient portrait is formed, and the patient experience is improved. The problem that data are mutually separated in a traditional management mode is solved, and comprehensive data support is provided for subsequent risk prediction and intervention decision making.

The invention relates to a dynamic health record automatic updating and management method based on a cloud platform. The method comprises the following steps: acquiring multi-source heterogeneous data and performing event packaging to generate a standard health event stream; performing cross-modal time sequence alignment processing on the standard health event stream to form a time sequence event sequence; an event-state double-layer time sequence knowledge graph is constructed, and real-time updating of health state vectors is achieved by means of the dynamic reasoning ability of a graph neural network in combination with a time sequence event sequence; and dynamic updating of the archive is completed through vectorization archiving. By the adoption of the method, the defects of traditional health archives in the aspects of data fusion depth, updating timeliness and state characterization precision can be effectively overcome, an accurate and real-time data basis is provided for chronic disease management and other long-term health intervention scenes, and the initiative and individuation level of health management is remarkably improved.

The invention discloses a multi-modal health prescription generation and simulation evaluation method based on an individualized digital twinborn body. The method comprises the following steps: firstly, constructing a multi-modal human body digital twinborn body model, representing individual physiological, pathological and behavioral states by hidden state variables, and training state transfer function and observation function parameters by using historical group data to form group-level twinborn bodies; and then performing parameter fine tuning in combination with multi-source health data of a new patient, and establishing an individualized digital twinborn body. The system generates multi-dimensional candidate prescriptions through a large model in combination with a medical knowledge base, carries out forward simulation prediction by using digital twin bodies, evaluates the curative effect, risk, cost and compliance of the prescriptions, and selects a final prescription by taking weighted utility or robust optimization as a criterion. According to the method, accurate and explainable prescription recommendation can be realized, closed-loop evaluation and safety monitoring are supported, and the method is suitable for the fields of individualized rehabilitation training, chronic disease management and clinical auxiliary decision making.

According to the general chronic disease patient dynamic monitoring and intervention management system, early recognition, early intervention and continuous management of chronic disease risks are achieved through fusion of multi-source physiological signals and behavior events, self-adaptive modeling of individual baselines, segmented judgment of risk evolution trajectories, self-adaptive generation of intervention strategies and closed-loop optimization of execution feedback. The system takes a whole-process closed loop of'monitoring-evaluation-intervention-feedback-optimization 'as a core, emphasizes individualization, time sequence and interpretability, and avoids dependence of static rules and a single model, so that the management accuracy and timeliness are remarkably improved.

The invention relates to the technical field of elderly chronic disease medication management, and discloses an elderly chronic disease medication compliance intelligent reminding and management system. The system comprises an individualized medication modeling unit for establishing an individual pharmacodynamic model containing medication taboo and dosage rules based on an electronic health record of a patient and pharmacokinetic parameters; the multi-modal behavior sensing unit is used for collecting physiological data and movement tracks through wearable equipment, and generating a medication behavior sequence in combination with the record of the intelligent medicine box; the compliance deviation analysis unit is used for comparing an expected behavior with an actual behavior to calculate indexes such as a time matching degree and dose deviation; the risk decision engine unit is used for outputting an intervention level and a reminding strategy in combination with the deviation index, the physiological state and drug interaction; and the cross-platform collaborative execution unit triggers multi-channel reminding, medicine box locking and emergency contact notification. According to the system, the whole medication management process is intelligent, and the medication compliance and safety of the elderly patients are improved.

The invention relates to the technical field of digital medical treatment, and discloses an intelligent chronic disease screening method based on multi-source data fusion, which comprises the following steps: firstly, calculating a sensor credibility reference value based on the historical performance and real-time state of each sensor; performing quality evaluation on the physiological signal according to the credibility reference value, and identifying and marking a signal disturbance interval; then, performing frequency domain analysis and environment interference correlation verification on the marked disturbance interval, and dynamically adjusting the weight coefficient of each sensor; carrying out weighted fusion on the multi-source physiological signals by adopting the adjusted weight coefficient, and carrying out baseline drift correction on low-quality signals; and finally, extracting physiological feature parameters from the fusion signal, matching the physiological feature parameters with a health state pattern library, and outputting a chronic disease screening evaluation result. The problems that in a traditional monitoring system, data quality evaluation standards are not uniform, fusion strategies are rigid, and continuous optimization capacity is lacked are solved, and the accuracy and reliability of chronic disease screening are remarkably improved.

The invention relates to the field of medical health and artificial intelligence, and discloses a tumor-chronic disease co-disease medication dynamic risk early warning method fusing AI and a rule engine, and the method comprises the steps: S1, collecting multi-source data of a co-disease patient, and generating unified feature data; s2, constructing a risk prediction model comprising a rule engine and a few-sample learning model; the rule engine performs preliminary judgment on the drug use risk; carrying out refined risk probability prediction on the few-sample learning model by utilizing a global category representation and feature matching mechanism constructed by the few-sample learning model; carrying out weighted fusion on prediction results of the two; s3, dynamically optimizing parameters of the few-sample learning model by adopting a meta-heuristic optimization algorithm; and S4, based on the optimized risk prediction model, carrying out real-time risk early warning on the medication data of the co-patient. According to the method, the technical problems of difficulty in prediction of a few-sample scene, low parameter optimization efficiency, unreliable early warning result and the like are effectively solved, and real-time monitoring, accurate evaluation and individualized early warning of the medication risk of the co-patient are realized.

The invention discloses a regulation mechanism research method of depleted precursor CD8T cells, and relates to the field of immunology and molecular biology. Comprising the following steps: detecting the expression level of the ARHGAP9 gene in a chronic virus infection model; constructing a T cell specific ARHGAP9 gene knockout animal model; the influence of ARHGAP9 deletion on the frequency, phenotype and function of the Tpex cell is analyzed; a single cell transcriptome sequencing technology reveals that ARHGAP9 regulates and controls a downstream signal channel of a Tpex cell. By constructing a chronic virus infection model, detecting the expression dynamic state of the ARHGAP9 gene, knocking out an animal model by utilizing T cell specificity ARHGAP9, and combining flow cytometry, qPCR, single cell transcriptome sequencing and other technologies, the invention discloses the effect of the ARHGAP9 as a novel immune checkpoint molecule, and provides a theoretical basis for developing Tpex cell targeting immunotherapy.

The application discloses a kind of based on deep learning's slow disease individualized development path planning method, it is related to intelligent health management technical field, including: the construction standardization health status dataset, and generate multidimensional feature set by representation learning process;With multidimensional feature set as the input of association analysis, extract the feature set representing different health goals, assign target weight to generate individual multi-objective health feature table;Individual multi-objective health feature table is input into deep reinforcement learning model, through the adaptive learning of multilevel sub-strategy, corresponding strategy weight is assigned to different health goals, and candidate intervention path is generated;In candidate intervention path training, risk and benefit feedback are combined, and dynamic balance intervention strategy is output.The application realizes the intervention contribution degree coordination and path weight adaptive convergence effect on the whole path scale to multiple health goals by using dynamic balance algorithm in candidate intervention path training process.

A device may receive physical activity data for a subject, wherein the physical activity data comprises raw acceleration data. A device may derive a plurality of metrics from the raw acceleration data, wherein the plurality of metrics comprise a plurality of pattern-related metrics. A device may input the plurality of metrics into a deployed machine learning model. A device may be configured to perform receiving, from the deployed machine learning model, a physical activity score. A device may calculate a risk score for at least one disease or condition based on the physical activity score.

The invention provides a curative effect driving type medical performance and payment system oriented to active medical treatment. Active medical economics and DIKWP (data / information / knowledge / wisdom / intention) five-layer semantic modeling are fused. The system semantically converts diagnosis and treatment behaviors into a semantic bill, constructs a target chain by taking a patient purpose as a core, continuously tracks causal contribution of each content unit to a curative effect, and quantifies a doctor service value by a semantic value increment; therefore, payment according to effects and remuneration according to effects are realized through a fair payment engine, medical insurance and an intelligent contract are compatible, and automatic settlement and dispute arbitration are realized. The system can be used for internet hospitals, chronic disease management and medical insurance payment reform, the medical quality, transparency and fund efficiency are improved, and supervision and policy optimization are supported.

The invention relates to application of an asparagine endopeptidase inhibitor in preparation of a medicine for improving insulin resistance. According to the application, starting from inhibiting the aging driving factor AEP, the AEP inhibitor is tried to be applied to the research of insulin resistance for the first time, and the AEP inhibitor is found to be capable of remarkably improving glucose tolerance and insulin sensitivity. Therefore, the invention provides the application of the asparagine endopeptidase inhibitor in preparation of the medicine for improving insulin resistance. Experiments prove that the AEP inhibitor not only can significantly enhance insulin sensitivity of old mice and improve glucose tolerance, but also can reduce serum low-density lipoprotein cholesterol and lactic dehydrogenase levels, shows multiple improvement effects on glucose metabolism and lipid metabolism, and also can be used for preparing the AEP inhibitor. After long-term administration, obvious toxic and side effects are not found in pathological examinations of blood routine examination, urine routine examination, heart, liver, spleen, lung, kidney and other important organs, so that the traditional Chinese medicine composition is proved to have good safety and is suitable for long-term intervention of chronic diseases.

The present invention relates to a composition for the improvement and treatment of muscle diseases and cachexia, wherein diflunisal has the effect of promoting the differentiation and proliferation of myogenic cells, and further has the effect of improving and treating cachexia induced by chronic diseases such as muscle diseases or cancer through effects such as increased muscle mass and recovery of muscle strength.

The application discloses a prognosis risk prediction model construction system and method based on chronic disease real world data, comprising a multi-source real world data acquisition and access module, a data standardization and quality control module, a multi-dimensional feature engineering and screening module, a comorbidity feature fusion and data set construction module, a prognosis risk prediction model training and optimization module, a model multi-dimensional verification and evaluation module, a model deployment and risk stratification output module; a special multi-source real world data acquisition and quality control system for chronic liver disease, diabetes and hypertension is constructed, data standardization processing is realized in combination with a chronic disease clinical diagnosis and treatment guideline, through multi-dimensional missing value, abnormal value processing and data deduplication integration, the quality and availability of the real world data are effectively improved, the core problem of disorderly and low-quality real world data in the prior art is solved, and a high-quality data source basis is provided for model construction.

The invention discloses a medication management system with dose adjustment and a medication management method, and the method comprises the steps: enabling a doctor to give a medication scheme according to the illness state of a patient, update an individualized medication scheme according to a medication compliance report of the patient, and upload the medication scheme to a cloud server through the arrangement of a doctor terminal; and the cloud server sends a generated patient medication compliance report to a doctor end. The patient end confirms the medication scheme and generates a medication plan scheme according to the medication scheme; the intelligent medicine box controls the target medicine bin to discharge medicine according to the medicine taking scheme and the medicine taking plan scheme. Through cooperative work among the doctor end, the patient end and the intelligent medicine box, an individualized medicine taking scheme based on the doctor end is realized, and a patient can automatically execute a new medicine taking plan scheme and record medicine taking behaviors, so that dynamic dose control is realized in the medicine taking process of the patient, data interaction and medicine taking compliance feedback between the doctor and the patient are completed, and the patient can take medicine conveniently. The safety and the standardization of long-term medication of patients with chronic diseases are improved.

The present application relates to the technical field of mutation recognition, in particular to a chronic disease sign mutation recognition method and system, comprising the following steps: acquiring data and calculating local trend reversal rate, generating stable domain boundary by using reversal rate amplitude difference value, segmenting according to the boundary and constructing trend amplitude asymmetry feature, calculating trend amplitude ratio and inputting kernel density estimation model to mark mutation candidate points, generating risk fluctuation deviation rate and defining mutation trigger domain based on sliding growth rate. In the present application, the stable domain boundary is generated by calculating the local trend reversal rate to realize dynamic segmentation, the trend amplitude asymmetry feature is constructed, the mutation candidate points are marked in combination with the kernel density distribution, and the trigger domain is defined by using the sliding growth rate, so that the nonlinear fluctuation law and the asymmetric evolution feature of the sign data are effectively quantified, the dependence on artificial threshold is eliminated, and the capture accuracy and state recognition reliability of weak mutation signals of multi-dimensional complex signs are improved.

The present invention relates to a system and method for recommending health functional foods to elderly patients with geriatric chronic diseases. According to the present invention, a method for recommending health functional foods, performed by a system for recommending health functional foods, comprises the steps of: receiving, from a target patient with a geriatric chronic disease, response data obtained from responses to a medical inquiry form or questionnaire; performing natural language processing on the response data; inputting the natural-language-processed response data into a first learning model to classify a mental disorder and a severity level of the target patient; receiving personal information and EMR data of the target patient; and inputting the personal information, EMR data , and information on the mental disorder and severity level of the target patient into a second learning model to recommend one or more health functional foods to the target patient.

This application provides a method, apparatus, and computer device for joint risk assessment of chronic diseases and comorbidities, relating to the field of medical and health technology. The method includes: performing hierarchical preprocessing on health examination-related data from multiple subjects to generate a standardized dataset containing single-disease labels and comorbidity combination labels; inputting the target data into a single-framework multi-objective collaborative ensemble model to obtain single-disease and comorbidity prediction values; the single-disease and comorbidity prediction values ​​are obtained synchronously through a single inference by the single-framework multi-objective collaborative ensemble model; calculating a comorbidity risk gain value based on the single-disease and comorbidity prediction values, and generating a structured assessment result based on the comorbidity risk gain value. This method can improve the accuracy of joint risk assessment of chronic diseases and comorbidities.

This invention relates to the intersection of traditional Chinese medicine modernization and intelligent healthcare, specifically falling under the category of pre-chronic disease screening and health management technology. More particularly, it relates to a method for diagnosing and treating pre-type 2 diabetes based on traditional Chinese medicine state theory, along with electronic devices and computer-readable storage media, constructing a complete, human-machine collaborative closed-loop diagnostic and treatment process. This method achieves objective data collection through portable devices using the four diagnostic methods of traditional Chinese medicine, utilizes a multi-algorithm fusion model to accurately identify the "potential pathological state" of pre-T2DM, and intelligently generates personalized intervention prescriptions based on this. Finally, it quantifies the effects and dynamically optimizes the treatment plan through data re-collection and model re-evaluation. This solves the problems of strong subjectivity in traditional Chinese medicine diagnosis and treatment, disconnection from modern technology, and lack of quantitative feedback and continuous adjustment after intervention. It achieves full-cycle, intelligent, and personalized health management for pre-T2DM, from identification and intervention to follow-up management.

The present invention is directed to the use of antiviral agents, in particular valomaciclovir stearate and its polymorph Form A, as well as H2G (omaciclovir) to treat multiple sclerosis in combination with other agents, as well as the use of these agents to treat diseases and conditions such as chronic mononucleosis, long COVID, chronic fatigue syndrome, fibromyalgia, Crohn's disease, ulcerative colitis, rheumatoid arthritis, systemic lupus erythematosus, Graves' disease, Alzheimer's disease, mesial temporal lobe seizures, Epstein-Barr-virus-linked autism, or an Epstein-Barr-virus-linked cancer.

The application discloses a traditional Chinese medicine body milk and a preparation method thereof. The traditional Chinese medicine body milk comprises the following components by weight: 5-15 parts of traditional Chinese medicine active ingredients derived from an ancient prescription of Danggui Yinzi, 1-3 parts of vitamin E, 0.5-2 parts of ceramide NP with a purity of greater than or equal to 95%, and 0.1-0.5 parts of natural borneol with a purity of greater than or equal to 99%, and the rest is an oil phase matrix, an aqueous phase matrix and an emulsifier. The traditional Chinese medicine active ingredients comprise angelica extract, astragalus polysaccharide, sophoramine and glycyrrhizic flavone, and are obtained by a low-temperature warm extract process at 40-45 DEG C. The preparation method comprises the following steps: raw material pretreatment, warm extract extraction, filtration and purification, precise compounding and fusion, emulsification and molding, and cooling and filling. The product has the advantages that skin irritation is less than 0.3, 24-hour moisturizing power is greater than or equal to 78%, the retention rate of herbal active ingredients is greater than or equal to 83.2%, and absorption is achieved within 5 minutes after smearing, and is specially used for improving the problems of dry skin, itching and difficult healing of wounds caused by blood deficiency and dryness caused by old age and chronic diseases.

The invention discloses a chronic disease patient diet management system, and the system comprises a data collection and integration module which is used for obtaining and fusing multi-source data, and forming a patient exclusive data set; the personalized diet scheme generation module is used for calculating and generating an initial diet scheme based on a medical rule base and the exclusive data set, and a final scheme is formed after the initial diet scheme is audited by the medical terminal; the diet scheme execution support module is used for assisting the user to adjust the diet scheme through a visual tool, a recipe library and an intelligent recording mode; and the monitoring and dynamic adjustment module is used for collecting diet execution data and physiological index data of the user, analyzing the relevance through a machine learning algorithm, generating an adjustment suggestion, and updating the scheme after confirmation of the medical end. According to the invention, multi-source data can be automatically integrated, a personalized diet scheme is intelligently generated and dynamically adjusted, and convenient execution support and continuous health management are provided for patients.

The application relates to the technical field of medical treatment and discloses a liver chronic disease screening method, a device and a storage medium.The method comprises the following steps: a liver detection system collects liver stiffness data and liver fat attenuation parameters of a target object; a mobile terminal system receives the liver stiffness data and the liver fat attenuation parameters, combines the liver stiffness data and the liver fat attenuation parameters with basic information of a preset target object, generates acquisition characteristic data, carries out label marking matrix processing on pretreatment integrated data of the cloud service system, generates a label marking matrix, carries out fitting residual processing on the label marking matrix, obtains extended decision tree data, carries out discrete sampling processing on the extended decision tree data, obtains a sparse vector, carries out classification calculation processing on the sparse vector, obtains a classification result, and generates a disease screening report based on the classification result.In the embodiment of the application, through system architecture and cloud service system data processing, the early warning timeliness and accuracy of liver chronic disease screening are improved.

This invention proposes a health monitoring method and device based on a smart bracelet. The method uses the smart bracelet as the executing entity and includes the following steps: S1: Acquiring a multimodal physiological signal set and an environmental interference data set of the monitored object. The multimodal physiological signal set includes electrocardiogram (ECG) signals, heart rate signals, blood pressure signals, blood oxygen saturation signals, skin conductance signals, and body temperature signals. The environmental interference data set includes motion state data, light intensity data, wearing contact pressure data, and ambient temperature data. S2: Preprocessing the multimodal physiological signal set and the environmental interference data set. This invention can achieve accurate, efficient, and personalized health monitoring, output health level and abnormality tracing information, and trigger early warnings when necessary. It is suitable for daily health management, exercise monitoring, and disease early warning scenarios for different groups such as children, adults, and patients with chronic diseases.

This invention relates to the field of medical data technology and discloses an AI-based method for managing cardiovascular chronic disease data. The method constructs a cardiovascular data pool containing patients' historical medical records and real-time physiological signals; establishes a dynamic ontology in the cardiovascular data pool based on a clinical knowledge graph, which defines the semantic relationships between different data entities; maps and generates data evolution trajectories on the dynamic ontology according to a preset treatment target path; activates an intelligent agent configured to traverse the dynamic ontology along the data evolution trajectory, resolving causal constraints between data entities based on the semantic relationships encountered during the traversal; the intelligent agent adaptively reorganizes new data flowing into the cardiovascular data pool in real time according to the resolved causal constraints, generating data slices with temporal correlation; inputs the data slices into a pre-trained deep learning network for feature fusion, outputting a dynamic profile of the patient's cardiovascular health status.