1110 results about "Diabetic patient" patented technology

The invention provides a real-time intervention method and system based on blood glucose prediction of a diabetic patient, and the method comprises the steps: obtaining blood glucose monitoring data and multi-dimensional life monitoring data based on blood glucose monitoring equipment and intelligent equipment, and carrying out the timestamp alignment of the multi-dimensional life monitoring data and the blood glucose monitoring data; inputting the aligned multi-dimensional life monitoring data and blood glucose monitoring data into a pre-trained blood glucose prediction model, outputting a first blood glucose level prediction value and a second blood glucose level prediction value, and generating a real-time intervention suggestion according to the first blood glucose level prediction value and the second blood glucose level prediction value; if the first blood glucose level predicted value or the second blood glucose level predicted value is lower than a threshold value, graded early warning is carried out, corresponding intelligent equipment is triggered to execute corresponding response measures, and the early warning mechanism can achieve early warning through a real-time, normalized and highly-personalized intervention strategy in combination with intelligent operation of the intelligent equipment. The life quality of a diabetic patient is obviously improved; and the blood sugar control is optimized.

The invention relates to the technical field of patient information processing, in particular to an intelligent health management system for diabetic patients, which comprises the steps of acquiring continuous blood glucose monitoring data of patients, extracting blood glucose values, timestamps and patient identity information, establishing a blood glucose data set, performing data cleaning on the blood glucose data set, and removing missing values and repeated values. According to the method, after the data are cleaned and analyzed, the generated blood glucose fluctuation index can reveal the blood glucose fluctuation condition in a short time, and therefore the possible health risk is predicted. The fluctuation indexes are standardized and compared with a set threshold value, potential abnormal blood glucose events are recognized, and the response speed and preventability of disease management are improved. Meanwhile, the health risk of the patient can be quantitatively evaluated through calculation of the abnormal accumulated score, early warning is automatically triggered when the abnormal accumulated score exceeds a safety threshold value, and intervention is carried out in time. And the management efficiency and the treatment pertinence of diabetes mellitus are improved.

The invention discloses a coronary artery calcification early warning system for type 2 diabetes patients, and relates to the technical field of medical detection. A data acquisition module is used for acquiring continuous physiological parameter data of a user; the risk modeling module is combined with coronary artery calcification evolution characteristics in historical clinical samples to construct a multi-parameter dynamic association model; an index weight calculation unit generates a risk influence factor vector based on a sensitivity analysis result of the physiological indexes on risk prediction; the machine learning analysis module performs iterative training on the prediction model by adopting an integrated learning algorithm, and performs prediction updating by utilizing a risk influence factor vector; the early warning trigger module dynamically generates a graded early warning signal according to the grading trend and a set threshold value; the weak item positioning module carries out contribution degree analysis and anomaly recognition on the key risk indexes and automatically generates personalized intervention suggestions; according to the invention, early recognition and dynamic early warning of coronary artery calcification progress can be realized, and the method is suitable for intelligent early warning management scenes of chronic disease cardiovascular risks.

The invention relates to the technical field of medical care informatics, in particular to a diabetic clinical data management system and method based on feature extraction, and the method comprises the steps: obtaining patient clinical data of a diabetic, the patient clinical data including patient blood glucose data and patient behavior data; behavior matching features among different behaviors of the diabetic patient are extracted through the patient behavior data; extracting correlation characteristics of different behaviors of the diabetic patient and blood glucose fluctuation through the patient blood glucose data and the patient behavior data; evaluating the blood glucose fluctuation risk according to the behavior matching features and the associated features of the diabetic patient, and performing blood glucose fluctuation risk early warning according to a blood glucose fluctuation risk evaluation result. According to the method, the accuracy and the reliability of blood glucose fluctuation risk early warning are effectively improved by quantifying the behavior matching degree among the dining behavior, the medication behavior and the movement behavior of the diabetic patient.

The invention provides a physiological data monitoring report generation method and system applied to diabetes management. The physiological data monitoring report generation method comprises the following steps: acquiring physiological monitoring data of a diabetic patient in a preset monitoring period; state fragment division and mode recognition are conducted on the physiological monitoring data, a time sequence state fragment set of the diabetic is obtained, metabolic load and steady-state migration quantitative characterization is conducted on the basis of the time sequence state fragment set, a metabolic evolution graph of the diabetic is generated, and the metabolic evolution graph of the diabetic is obtained by combining a preset medical knowledge base and historical health data of the diabetic. Semantic analysis is carried out on the metabolic evolution map, and a health condition key insight set of the diabetic patient is output; and according to the personalized context and expression preference of the diabetic patient, converting the health condition key insight set into a health guidance report conforming to the personalized context and expression preference. The accuracy, comprehensiveness and user acceptability of the physiological data monitoring report in diabetes management can be effectively improved.

The invention belongs to the technical field of diabetic foot ulcer early-stage active protection, and particularly relates to a diabetic foot gait training system based on tactile sense direction guidance. The system comprises an intelligent insole system, a cloud platform, a flexible tactile feedback interface and mobile terminal software. The intelligent insole system collects plantar pressure data in the gait process of a user and sends the pressure data to the cloud platform and the mobile terminal software through wireless communication; the cloud platform calculates sole accumulated pressure distribution in a specified time period, and when the accumulated partial pressure distribution is abnormal, control data is sent to the flexible tactile feedback interface through Wi-Fi; the flexible tactile feedback interface receives data of the cloud platform to generate tactile direction guidance, and gait training is achieved; and mobile terminal software can check a plantar pressure cloud picture and a pressure curve in real time. The training system generates direction prompts through tactile codes, guides a patient to actively adjust abnormal gaits, changes plantar pressure distribution and reduces the morbidity of diabetic foot ulcers.

The invention relates to a metabolic marker for diagnosing diabetic secondary osteoporosis and application of the metabolic marker, and belongs to the technical field of biological medicine. The method comprises the following steps: firstly, screening out obviously different metabolites between osteoporosis (DOP) and osteoporosis-free groups (DM) in diabetic patients; and secondly, screening out significant difference metabolites between the primary osteoporosis patient and the healthy control group, and excluding the intersection of the two groups of difference metabolites to obtain metabolites which exclude the influence of the primary osteoporosis. Metabolic markers, namely cytosine nucleotide, ketoglutaric acid and triethylamine, are obtained through further screening, and when the three metabolic markers are independently used, AUCgt; the AUC during combined use is 0.99, so that the limitation that the sensitivity of the traditional bone mineral density detection on the osteoporosis recognition in the diabetic population is insufficient is effectively made up.

The invention discloses a diabetes complication risk prediction method, device and system. The method comprises the following steps: acquiring multi-dimensional medical data of a diabetic patient in a target time period; performing feature enhancement on the multi-dimensional medical data according to a preset medical knowledge graph to obtain an individualized feature set; performing feature weighted fusion on the enhanced features of different dimensions in the individualized feature set to obtain individualized features corresponding to the multi-dimensional medical data; analyzing the multi-dimensional medical data from the time dimension by adopting a time sequence model to obtain the change trend of the development process of different complications along with time; and analyzing and processing the personalized features and the change trend by adopting a complication risk prediction model to obtain a complication risk result. According to the method and the device, the technical problem that the accuracy of risk prediction of complications of diabetes mellitus is low due to the fact that medical data of different dimensions are difficult to integrate effectively and time dimension information is ignored in related technologies is solved.

The invention belongs to the technical field of artificial intelligence, and discloses a diabetic complication risk deduction system based on a graph neural network and a knowledge graph, and the system comprises a physiological feature sensing module which collects physiological sensing data of a diabetic patient, carries out the time synchronization and normalization processing of the physiological sensing data, and carries out the prediction of the diabetic complication risk. Separating a stability characteristic component and a disturbance characteristic component; the pathological potential mapping module is used for mapping the stable characteristic component into a basic physiological node and mapping the disturbance characteristic component into a potential pathological activation node on the basis of a semantic structure of a knowledge graph, introducing a pathological potential function and establishing a pathological potential mapping network; the pathology activation recognition module calculates potential pathology energy of each potential pathology activation node in the pathology potential mapping network through a graph neural network, and when the potential pathology energy is greater than a preset potential pathology energy threshold, the potential pathology activation node is marked as a pathology activation source node; and early warning of diabetic complications is realized.

The invention relates to a diabetic symptomatic ICAS prediction method and system based on machine learning. The method comprises the following steps: collecting an HRMRI image sample of a target patient; performing high-risk plaque feature analysis on the HRMRI image sample to obtain high-risk plaque features; performing image omics feature extraction and screening on the HRMRI image samples to obtain image omics features; based on an XGBoost algorithm, training the high-risk plaque features and the radiomics features to obtain a prediction model; and performing symptomatic ICAS attack prediction on a to-be-predicted patient through the prediction model to obtain a prediction result. The accuracy and the stability of the clinical model in predicting the new symptomatic intracranial atherosclerotic disease of the diabetic patient can be improved, and the clinical model adapts to individual specificity.

The invention discloses a diabetic patient data management method and system based on big data. The method comprises the steps that basic information and stable blood glucose data of a patient are collected, similar cases are matched through a cloud database to obtain influence factors, and a personalized data collection scheme is generated; customizing a user terminal and establishing a data management container, adaptively acquiring blood glucose and influence factor data, calculating a change degree through a sliding window algorithm and performing early warning; and dynamically updating influence factor weights by adopting a time-varying weight model, and generating an intervention strategy according to the weights. The system comprises a cloud server, a comprehensive management terminal, a user terminal and other modules, and realizes data acquisition-analysis-intervention closed-loop management. According to the scheme, through big data analysis and the dynamic weight model, personalized accurate management of the diabetic patient is realized, the acquisition strategy and the intervention scheme can be adjusted according to individual differences, and the risk early warning accuracy and the management efficiency are improved.

The invention discloses an ultrasound-based diabetic renal lesion risk detection method and system, and relates to the technical field of ultrasonic detection.According to the system, through continuous scanning of a two-dimensional ultrasonic probe, an elastic imaging probe and a Doppler probe, the shape, structure, blood flow characteristics and cortex images of the kidney are obtained; an image processing technology is adopted for preprocessing, space registration and fusion, and a kidney area is automatically segmented; according to the extracted kidney tissue characteristics, calculating an atrophy risk coefficient and a fibrosis risk coefficient, and comparing with a standard threshold value to evaluate the kidney lesion risk; calculating a comprehensive risk coefficient in combination with blood glucose, urine protein and diet data, and evaluating diet influence risks; a renal lesion risk assessment model is constructed by using a convolutional neural network and a machine learning algorithm, a renal lesion prediction function is generated, and the system can accurately assess the renal lesion risk of a diabetic patient and provide early diagnosis and personalized treatment suggestions.

The invention relates to the technical field of medical health management, in particular to a chronic kidney disease and diabetes collaborative nutrition monitoring and early warning method and system.The method comprises the steps that multi-dimensional physiological indexes, nutrition intake and sleep data of a patient are obtained, and a dynamically-coupled metabolic state topological space is constructed; based on the space, constructing a dual-disease metabolism coupling matrix, analyzing stability characteristics of a metabolism state trajectory, identifying stable and unstable attractors, and calculating a multi-dimensional risk score; a sleep-metabolism synchronism risk prediction model is introduced, and a personalized early warning threshold value is dynamically adjusted; when the risk score exceeds a threshold value, early warning is triggered, and intervention suggestions are generated, the interactive influence of protein metabolism and glycometabolism is quantitatively described from the metabolic mechanism level, and the contradiction of chronic kidney disease and diabetic patients in nutrition management is solved; limitation of a static threshold is broken through, and dynamic risk assessment is realized; sleep factors are creatively integrated, and a new way for improving the metabolic state is provided.

The invention relates to application of sea cucumber-derived nano-vesicles in preparation of a medicine for promoting wound repair. The nano-vesicles (SC-EVs) extracted and prepared from sea cucumbers can effectively promote cell migration, scratch repair and tube forming capabilities of human skin epithelial cells (HACAT) and human skin microvascular endothelial cells (HM DEC) and inhibit cell apoptosis, so that the capability of repairing cells is achieved, and particularly the effect of repairing wound surfaces in a high glucose state or diabetes mellitus is achieved. The invention provides a new direction and thought for the application of the sea cucumber-sourced nano vesicles (SC-EVs), provides a brand new drug research and development and clinical treatment direction for wound repair, especially for solving the problem of difficult healing of wounds of patients with high glucose or diabetes, and has extremely high social value and market application prospect.

The invention relates to the technical field of medical data processing, in particular to a diabetes data sharing and analysis system based on a cloud platform, which comprises a diabetes data co-processing module, a patient feature hierarchical management module, a diabetes associated path analysis module and an intelligent semantic reasoning module. According to the invention, data of diabetic patients in medical institutions are collected through the cloud platform, data accuracy and effectiveness are improved, interference factors are eliminated, multi-source data are combined, redundancy and conflicts are reduced, cross-institution data integration capability is improved, patient features are processed in a layered manner through feature matching and priority ranking, a layered feature network structure is formed, and the accuracy and effectiveness of the data are improved. The node association strength and the path relation are analyzed, weak nodes are removed, interaction nodes are expanded, the path analysis depth and research value are improved, keywords and paths are matched based on semantic reasoning, high-frequency nodes and semantic paths are screened, the query accuracy and practicability are optimized, and the data analysis intelligent level and management efficiency are enhanced.

The invention belongs to the technical field of blood glucose detection, and particularly relates to an intelligent 5G blood glucose detector and a using method.The intelligent 5G blood glucose detector is characterized in that an inflatable finger sleeve is arranged on the front side of a shell, and an air pump, an infrared detection probe, detection test paper, a detection device and a control circuit board are arranged in the shell; the detection device comprises a guide power mechanism, a blood glucose detector and a telescopic rod, a pressure sensor is arranged in an inflatable finger sleeve, blood glucose can be conveniently and rapidly detected, data can be transmitted to a cloud background through a 5G module on a control circuit board, uploading and recording of detection data are achieved, and a patient and a doctor can check the detection data; the illness state control of diabetic patients is promoted. The detection test paper in the 5G intelligent blood glucose detector is specially-made triplet type test paper, continuity of three times of work of disinfection, blood sampling and binding of blood glucose detection is achieved, troubles caused by frequent different operations of a user are reduced, and blood glucose of a patient with hyperglycemia can be efficiently, conveniently and safely collected at any time.

The invention provides a chronic disease risk early warning system and method based on artificial intelligence. The method comprises the following steps: acquiring historical monitoring information of a diabetic patient; determining development trends of different disease course stages through historical monitoring information, and performing trend evolution based on all the development trends to obtain evolution characteristics of each disease course stage; determining the risk contribution degree of each health index to the chronic disease risk according to the linear correlation among different health indexes, and determining a risk monitoring model of the target patient through all the risk contribution degrees; performing confidence adjustment on the chronic disease risk in the risk monitoring model according to each evolution feature, and further obtaining a confidence risk value of the current disease course stage of the target patient; and carrying out risk prompting on the target patient based on the confidence risk value. By adopting the scheme of the invention, real-time modeling can be carried out on the dynamic change of the course of disease of the patient under the background of massive health data.

A multifunctional plant lactobacillus plantarum and a use thereof. The plant lactobacillus plantarum LY4 strain was stored in the Guangdong Microbial Culture Collection Center on 19 April, 2023, with the strain deposit number of: GDMCC N0: 63373. The LY4 strain has high inhibition capabilities with regard to key enzymes α-glucosidase and α-amylase which cause blood sugar to rise, with an inhibition rate reaching as high as 100%, an effect reaching 96% or more of the diabetes drug Acarbose, and the ability to degrade lipopolysaccharides; moreover, the LY4 strain has good resistance to acid and bile salt, improved safety and thermal stability, can be used as an efficient strain for controlling blood sugar, can degrade lipopolysaccharides while relieving diabetes, and provides a new choice for controlling blood sugar in diabetes patients.

The invention relates to the field of remote medical management, and particularly discloses a remote medical management system and method based on a cloud platform. The method comprises the following steps: firstly, acquiring basic information of a diabetic patient acquired by a database, doctor's advice information of the diabetic patient acquired by the database and blood glucose values of the diabetic patient acquired by a portable device at a plurality of preset time points, then performing feature extraction and correlation analysis on the three information by using a deep learning technology, and finally, generating the blood glucose values of the diabetic patient through a generator. Therefore, a doctor is helped to quickly know the current health condition and the treatment effect of the patient, the treatment scheme can be conveniently and timely adjusted, personalized medical treatment is realized, and continuous optimization of long-term health management is promoted.

The invention discloses a system and a method for intelligently monitoring blood sugar of a patient before and after diet based on food components, and belongs to the technical field of blood sugar monitoring, and the system comprises a food component quantification module which is used for converting a food image or description into accurate macro nutrient and micronutrient data; the multi-modal data fusion module is used for preprocessing and fusing the multi-modal data of the patient; the patient blood sugar prediction module is used for constructing a patient blood sugar prediction model to analyze the multi-modal fusion data of the patient and predicting the blood sugar of the patient; and the difference analysis optimization module is used for comparing actual blood glucose with predicted blood glucose after meal and analyzing difference reasons. The problems that blood glucose of a patient before and after eating cannot be intelligently monitored, and accurate quantification and prediction of the relation between diet and blood glucose cannot be achieved are solved. The system can intelligently monitor the blood glucose of the patient before and after eating, can help the diabetic to realize accurate quantification and prediction of the relationship between diet and blood glucose, and can effectively control the blood glucose and reduce the risk of complications.

The invention relates to a diabetes exercise rehabilitation and diet collaborative management method, device and equipment and a medium. The method comprises the following steps: acquiring individual feature data, physiological data and daily behavior data of a diabetic patient, and constructing a personalized portrait of a user; generating an exercise plan and a diet plan according to a set period based on the personalized portrait of the user; in response to the obtained real-time user data, dynamically correcting the exercise plan and the diet plan to obtain an exercise and diet collaborative plan; the real-time user data comprises blood glucose data, diet intake data and exercise data; the exercise and diet collaborative plan comprises exercise items arranged based on time points and diet arrangement; generating a corresponding push instruction according to the time point; the pushing instruction is used for indicating to push corresponding exercise items or diet arrangements to the terminal of the diabetic patient. By adopting the method, the problem of inadaptation caused by a rigid fixed plan can be avoided by an exercise-diet collaborative intervention mechanism, and the management response capability of external variable change is improved.

The invention relates to the technical field of medical information processing, in particular to a type 2 diabetes precise typing and dynamic treatment system which comprises a clinical feature extraction module, a metabolism dynamic modeling module, a precise typing module, a treatment parameter generation module and a dynamic treatment output module. Wherein the clinical feature extraction module is used for collecting a multi-dimensional clinical data set of a patient for continuous 14 days; the metabolism dynamic modeling module is used for extracting blood glucose fluctuation characteristics and pancreas islet function compensation characteristics; the precise typing module is used for dividing metabolism-driven subtypes to which the patients belong through a clustering algorithm; the treatment parameter generation module is used for mapping a subtype-treatment rule base and outputting a matched personalized treatment parameter combination; and the dynamic treatment output module outputs a dynamic treatment scheme. According to the invention, through a dynamic treatment mechanism based on typing, linkage control of individual difference identification and real-time adjustment of the medication scheme of the type 2 diabetes patients is realized, and the accuracy and adaptability of treatment are improved.

The embodiments described herein may relate to methods and systems for adjusting insulin delivery. Some methods and systems may be configured to adjust insulin delivery to personalize automated insulin delivery for a person with diabetes. Such personalization may include receiving and / or determining one or more user specific dosage parameters.

The invention relates to the technical field of gene detection, in particular to application of a DNA methylation site in diagnosis of type 2 diabetes mellitus, a kit and a method thereof. Clinical detection proves that the methylation site cg06087988 has significant difference between a type 2 diabetes mellitus patient and a non-type 2 diabetes mellitus individual, and can act on diagnosis / prediction of type 2 diabetes mellitus.

The invention discloses a diet intervention scheme generation method, and belongs to the technical field of diabetic diet intervention, and the method comprises the following steps: obtaining related information of a target user, calculating a BMI of the user, carrying out mode selection, inputting designated information by the user, carrying out automatic calculation by a program, and automatically matching a diet scheme. According to the method, the traditional diet intervention process of a nutritionist is abandoned, relevant data can be quickly obtained through BMI calculation, the matched diet scheme can be automatically obtained through mutual combination of the relevant data and the collected information data, the diet intervention cost can be greatly reduced through the method, the operation process is simple and easy to implement, and the method is suitable for popularization and application. According to the method, most diabetic patients can obtain effective materials, and the method can be customized according to the dietary habits of the diabetic patients on the premise of safety, so that the application effectiveness and scientificity of the method are greatly improved.

Certain aspects of the present disclosure provide a monitoring system comprising a continuous analyte sensor configured to penetrate a skin of a patient and generate a sensor current indicative of analyte levels of the patient, and a sensor electronics module coupled to the continuous analyte sensor. The sensor electronics module comprises an analog to digital converter configured to receive the sensor and convert the sensor current generated by the continuous analyte sensor into digital signals, one or more processors configured to convert the digital signals to a set of analyte measurements indicative of the analyte levels of the patient, and a Bluetooth antenna configured to transmit the set of analyte measurements wirelessly to a wireless communications device using Bluetooth or BLE communications protocols.

The invention relates to a diabetes management system, equipment and a medium. The method comprises the steps that a data processing module collects multi-modal medical data, dynamic time sequence features are extracted through a graph neural network to label semantic tags, and a structured patient information data set is formed; in the database construction module, a data fusion unit performs cross-modal feature alignment, redundancy elimination and feature enhancement on the structured data set, and establishes a dynamic and unified health record database; and the risk assessment unit extracts individual features by adopting an improved random forest algorithm based on the database, and generates a personalized health risk assessment report. And the health management module customizes a health management scheme through a generative adversarial network according to the evaluation report, and dynamically updates intervention measures according to real-time monitoring data. By adopting the system, the accuracy and individuation degree of risk prediction can be improved, accurate and dynamic whole-cycle health management service is provided for diabetics, and the management efficiency and the medical quality are effectively improved.

The invention belongs to the technical field of medical health, and particularly relates to a type 2 diabetes mellitus cognitive hypofunction prediction system and method. By monitoring and analyzing the cognitive function impairment condition of the type 2 diabetes patient in real time, the cognitive function decline trend can be found in time, so that a scientific basis is provided for clinical diagnosis and treatment, the safety boundary of cognitive function decline can be effectively predicted through accurate image processing and data analysis, and the cognitive function decline trend can be found in time. In addition, the method can accurately evaluate the level of cognitive hypofunction according to the diffusion tensor of cognitive function impairment, provides important reference for doctors to formulate treatment schemes, and is finally beneficial to improving the living quality of the type 2 diabetes patients and reducing the risk of cognitive impairment.

The invention, which relates to the field of diabetes nursing, discloses a multi-model fusion personalized diet recommendation system for diabetes mellitus, comprising a recipe recommendation decision module based on a system architecture, a blood sugar prediction module, and an intelligent question and answer interaction module. The nutritional requirements and the blood sugar control target of the patient are met at the same time; the blood glucose prediction module predicts the postprandial blood glucose level according to the current blood glucose level of the user and the recommended recipe; the intelligent question and answer interaction module provides convenient natural language interaction, collects user feedback and dynamically adjusts recommended content. Compared with the prior art, the method has the advantages that scientific diet recommendation is provided, the recommendation result is optimized through a user feedback mechanism, and the recommendation accuracy and practicability are ensured; a convenient health management tool is provided, and the compliance of the patient on diet management is enhanced; the health condition of the diabetic patient can be improved, the burden of a medical system is relieved, and the public health level is improved.