802 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 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 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 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 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.

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 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.

The invention discloses a diabetic cardiomyopathy clinical prediction model, an establishment method and application thereof. The method includes the steps that clinical data of type II diabetes patients meeting the containing and discharging standard are collected based on public health data and processed, a random forest RF prediction model is established, the random forest RF prediction model is trained and evaluated to finally establish the random forest RF prediction model, the model accuracy is 0.889, the sensitivity is 0.896, the specificity is 0.880, and the recall rate is 0.896. The model has higher prediction accuracy and robustness, and can be used for an intuitive and convenient webpage calculator for early diagnosis of the diabetic cardiomyopathy, so that a clinician can easily predict the risk of the diabetic cardiomyopathy, resources are allocated according to a prediction result, and waste of medical resources is reduced.

Described are techniques, processes, devices, computer-readable media that enable provision of an indication of whether it is safe for a person with diabetes to participate in exercise while using a wearable drug delivery system. A processor may receive or obtain physiological data related to a condition of a wearer of the wearable drug delivery system and by evaluating an exercise model that uses inputs related to the physiological data to make the determination of whether it is safe to exercise and output an exercise safety signal. Modifications to the wearer's medication treatment plan and other actions may be based on an outputted exercise safety signal.

The invention relates to the technical field of physiological measurement, in particular to a diabetes symptom automatic detection system which comprises a blood glucose fluctuation analysis module, a polydipsia and polyuria symptom detection module, a decline level evaluation module and a progress stage recognition module. According to the invention, through the correlation analysis of real-time blood glucose monitoring and the diet state of the patient, the change of the blood glucose level is accurately captured, the development trend of the illness state is predicted, the real-time performance of illness state monitoring is enhanced, the medical intervention is more timely and effective, and through the continuous monitoring of the water intake and the urine volume of the patient, the polydipsia and diuresis symptoms of the diabetic patient are timely found, and the patient experience is improved. The correlation between a target symptom and the development of diabetes is determined through data analysis, the recognition capability of early-stage diabetic complications is enhanced, the organ function level is evaluated, the signs of organ damage are recognized, the treatment strategy is helped to be adjusted, the development of complications is prevented, the real-time performance and accuracy of disease monitoring are improved, and the disease diagnosis and treatment efficiency is improved. And the complexity and the cost of long-term treatment are reduced.

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. Some methods and systems may be configured to adjust insulin delivery to a person with diabetes according to one or more conditions of an insulin delivery device. Some methods and systems may be configured to enable a lock-out mode where adjustment to insulin delivery to personalize automated insulin delivery is restricted.

The invention pertains to the technical field of biomedical engineering and pharmacology and, without being limited thereto, specifically relates to a composition that enables the healing of full-thickness and chronic wounds (particularly in the foot region) observed in individuals suffering from diabetic disease.

The invention discloses a diabetic shoe system for plantar pressure monitoring and pressure reduction of a diabetic patient, relates to the technical field of diabetic foot monitoring and rehabilitation, and provides the following scheme that the diabetic shoe system comprises a monitoring shoe, and a plantar pressure monitoring mechanism is laid on the upper surface of the interior of the monitoring shoe; a plantar pressure distribution and pressure reduction mechanism is arranged on the upper surface of the plantar pressure monitoring mechanism, and a bridle mechanism is arranged above the plantar pressure distribution and pressure reduction mechanism. According to the technical scheme, the sole pressure distribution and pressure reduction mechanism composed of the multiple array type air bags is arranged on the sole, the pressure of the sole is monitored by means of the multiple flexible pressure sensors arranged in an array mode, the sole is controlled to incline forwards or backwards or be horizontal by means of different expansion degrees of the multiple air bag sets while the pressing force of the sole is monitored, and therefore the comfort degree of the sole is improved. In a mode of transferring the gravity center of the sole, the pressure is transferred to the heel when the stress of the sole is large, so that ulcer caused by overlarge stress of the half sole of the diabetic foot is prevented.

The invention discloses a composite double-protein hypoglycemic peptide as well as a preparation method and application thereof, and belongs to the technical field of animal and plant source double-protein active peptides. The method comprises the following steps: performing computer simulation enzyme digestion and virtual screening by utilizing bioinformatics to determine a protein raw material and protease, performing enzymolysis on animal and plant double proteins (soybean protein and casein) serving as raw materials by adopting neutral protease, and performing separation, purification and structural identification, thereby obtaining the protein. The protein peptide fragment with high alpha-glucosidase inhibitory activity is obtained by virtually screening molecular docking, and the amino acid sequences of the protein peptide fragment are shown as SEQ ID No: 6, SEQ ID No: 9 and SEQ ID No: 24. The compound double-protein peptide sequence provided by the invention has an inhibiting effect on the activity of alpha-glucosidase, can be used for preventing and treating diabetes mellitus, and can be used for long-term health care or treatment of people with impaired glucose tolerance or diabetics as a functional food ingredient.

The invention belongs to the technical field of data processing, and particularly relates to an intelligent diabetes blood sugar monitoring method and system based on artificial intelligence and a medium, the method comprises the steps of collecting personal information of a diabetic patient, periodically monitoring physiological data of the diabetic patient, and converting the personal information; classifying the diabetic patients, analyzing the physiological data of different types of diabetic patients to obtain an analysis result, calculating a correlation score of each physiological index and the blood glucose level based on the analysis result, and obtaining a first physiological index based on the correlation score; training the learning data by using a machine learning algorithm to generate a prediction model of a corresponding type; the intelligent wearable device is used for measuring physiological data of a patient to be monitored, the prediction model of the corresponding type is obtained, the prediction model outputs the predicted blood glucose level, and whether the blood glucose level monitored by the intelligent wearable device is accurate or not is judged based on the predicted blood glucose level. The blood glucose monitoring accuracy of the diabetic patient can be improved.

The invention discloses application of N-acetyl serotonin in preparation of a medicine for treating type 2 diabetes mellitus, and belongs to the field of biological medicine. In-vivo and in-vitro experiments prove that the N-acetyl serotonin can promote secretion of GLP-1, has the effects of obviously reducing blood sugar, being good in sugar tolerance and insulin tolerance, and is obviously improved aiming at high fasting blood glucose, high postprandial blood glucose, poor glucose tolerance and reduced insulin tolerance; the GLP-1 secretion insufficiency is obviously improved. The invention is of great significance in maintaining blood sugar stability and improving glucose and insulin tolerance of diabetic patients, and provides a new solution for treatment of type 2 diabetes mellitus.