3612 results about "Glucose blood" patented technology

A blood glucose sensing system includes a plurality of physiological sensors. The system can estimate blood glucose based on discrete invasive blood glucose estimates from a blood sample, discrete noninvasive blood glucose estimates derived from optical sensors, and continuously-calculated blood glucose estimates derived from a nonlinear state-space model of glucose and insulin reactions within a human body. The state-space model has user-entered values corresponding to their insulin and meal intake. The user's blood glucose is estimated from a combination of the discrete invasive blood glucose estimates, the discrete noninvasive blood glucose estimates and the continuously-calculated blood glucose estimate.

The invention relates to the technical field of child health monitoring and artificial intelligence, and provides a T1DM child blood glucose monitoring and early warning system. An existing blood glucose monitoring system is generally only based on single blood glucose data acquisition, and blood glucose fluctuation of children in different situations is difficult to accurately capture in real time. Through a multi-modal data fusion technology, multi-modal physiological features are integrated, and deep feature extraction and fusion analysis of data are realized. The system further combines a Q-learning algorithm to generate a personalized blood glucose early warning strategy, and utilizes a hidden Markov model (HMM) to identify the behavior situation of the child and optimize the early warning threshold setting. Meanwhile, data privacy is protected in a data distributed processing process through a federated learning framework and a differential privacy protection technology. The real-time performance and accuracy of blood glucose monitoring are improved, and intelligent and personalized health management and early warning services are provided for children and guardians of the children on the premise of protecting the privacy of the children.

The invention discloses an insulin infusion decision-making system and method combining reinforcement learning and metabolism simulation, and belongs to the field of clinical decision-making assistance. Comprising an acquisition processing module, a metabolism simulation module, a dynamic updating module, a path simulation module, a strategy control module, a monitoring constraint module, a decision analysis module, a correction processing module, a training optimization module and an injection control module. The method can accurately reflect the metabolic characteristics of the patient, makes the insulin decision more targeted, realizes automatic adjustment of the model, keeps real-time perception and response to the state of the patient, avoids the risk caused by model aging or error accumulation, enhances the long-term control ability, improves the control efficiency while guaranteeing the blood glucose stability, and reduces the cost. The fault-tolerant capability of the system is improved, the interpretability and safety guarantee of clinical decisions are improved, and the credibility of doctors is enhanced.

The invention discloses a multi-parameter fusion blood glucose prediction method and system. The method specifically comprises the steps that continuous blood glucose monitoring data, physiological feature data and behavior data of a to-be-tested person are obtained; inputting the blood glucose time sequence data into a trained time sequence prediction model to obtain blood glucose time sequence data; fourier transform is carried out on the multi-dimensional feature vector to obtain a multi-dimensional feature vector; constructing a multi-layer feature fusion structure; based on the multi-layer feature fusion structure, the blood glucose time sequence data are fused, and a blood glucose predicted value is output. According to the technical scheme, the blood glucose prediction accuracy is remarkably improved by accurately capturing the blood glucose change trend and periodicity.

The invention provides a blood glucose monitoring and early warning method. The blood glucose monitoring and early warning method comprises the steps of a, collecting a skin surface spectral signal I (lambda), b, collecting skin surface temperature T, humidity H, environment light intensity L and environment electromagnetic interference E parameters, c, preprocessing the spectral signal I (lambda), removing noise N (I) through wavelet transform, and d, calculating the blood glucose monitoring and early warning parameters. And the signal intensity is corrected through an ambient light intensity compensation model. According to the blood glucose monitoring and early warning method, a non-invasive acquisition mode combining near infrared spectrum and visible light spectrum is adopted, and a multi-modal regression model and a dynamic interference compensation technology are innovatively introduced, so that the problem that the precision of a sensor is influenced by skin temperature, humidity and environmental electromagnetic interference in the prior art is solved; the timeliness and the stability of signal purification are improved by utilizing preprocessing and optimization algorithms of spectral signals, such as wavelet transform, empirical mode decomposition and fast Fourier transform.

The invention relates to the technical field of health early warning, in particular to a human health early warning method and system based on physical examination data analysis, which comprises the following steps: collecting physical examination data of a human body, analyzing index changes of blood pressure, blood sugar, cholesterol and body mass index of the human body, sorting the physical examination data of the human body through time sequence sorting, and generating a time sequence sorting result. According to the method, time sequence sorting and spatial dimension evaluation are performed on the physical examination data, so that the health risk can be accurately identified and the early warning level can be dynamically evaluated, the data analysis precision is improved, the risk prediction dimension is increased, early warning is more timely and accurate, and the accuracy of early warning is improved by performing layout evaluation on the physical examination data points in a finite dimension space. Potential health abnormalities can be identified on multiple levels, and multi-dimensional analysis provides a new way for identifying complex health problems, so that health management is more active and predictable, health monitoring and risk management are optimized, and the health condition of a patient is improved.

Hydrogel comprises hydroxyapatite nanowires and a base material for forming the hydrogel, and further comprises glucose oxidase (GOx), gallic acid, magnesium ions (Mg < 2 + >) and the like, the gallic acid and the Mg < 2 + > form Mg-MOF, and the GOx is loaded on the Mg-MOF, so that the diabetes microenvironment of a bone defect part is locally adjusted, and bone defect repair is facilitated. Through verification, the sustained-release gallic acid of GOx-MOF participates in cascade reaction, GOx is removed to generate H2O2, the benefit is maximized, and the sustained-release gallic acid has good effects of controlling blood glucose, removing ROS, regulating inflammation and promoting osteogenesis and angiogenesis. The research is expected to play a role in microenvironment regulation of diabetes mellitus bone regeneration and minimally invasive treatment of bone defects.

The invention relates to the technical field of intelligent data management, in particular to a multi-source data fusion type intelligent data management system based on edge computing. The system comprises a multi-modal data acquisition unit which acquires multi-source heterogeneous data in real time; the edge preprocessing and enhancement unit integrates a long short-term memory network and a cross attention mechanism, and the lightweight anomaly detection unit deploys a structured pruning and quantized double-branch hybrid model at edge nodes, and performs real-time classification and anomaly scoring on multi-modal fusion feature tensors; and the grading early warning unit is used for receiving the fused comprehensive abnormal score, mapping the comprehensive abnormal score into a discrete alarm grade, and generating a standardized message body containing the grade, a timestamp and details. According to the design, through local tolerance self-adaption and LSH Hash accelerated matching, the coupling complexity of the electrocardio-blood pressure-blood glucose multiple signals is quantified, and the problem that the complex coupling relation among multiple physiological parameters is difficult to capture through a single signal is solved.

The invention discloses a dynamic blood glucose monitoring and intelligent pump speed adjusting system and method, and relates to the technical field of biomedical engineering.The dynamic blood glucose monitoring and intelligent pump speed adjusting method comprises the steps that precise monitoring is conducted through a multi-mode sensing module, an enzyme electrode sensor adopts an anti-interference coating, a Raman spectrum dynamic correction algorithm is combined, and motion artifacts and temperature drift are restrained; the metabolic feature fusion calibration module is integrated with a time convolution network, time sequence modeling is carried out on diet, exercise and historical blood glucose data, an individualized metabolic parameter database is constructed, and self-adaptive signal drift compensation is achieved; the self-adaptive pump control module innovatively designs a double-layer fuzzy PID controller, a base layer generates an infusion rate according to a real-time blood glucose state and insulin activity, an optimization layer introduces an LSTM metabolism prediction model, and the slope of an infusion curve is dynamically adjusted in combination with future behavior pre-judgment; in the edge-cloud collaborative architecture, a lightweight TCN model is locally deployed to guarantee real-time control, and a cloud end optimizes a global digital twinborn model through federal learning and generates a multi-dimensional metabolic risk early warning report.

The invention relates to an integrated management system for diabetic patients, which comprises the following steps of: constructing a high-dimensional metabolic data model, performing multi-modal fusion on genome information, metabolome data and life behavior data, and mining a potential mode of metabolic characteristics of the patients; carrying out patient typing by utilizing a clustering algorithm comprising a self-organizing mapping network, and identifying different metabolism types comprising an insulin resistance type and an insulin secretion insufficiency type; developing a hierarchical management strategy based on a typing result, wherein the hierarchical management strategy comprises diet, exercise and medicine intervention schemes for different types; collecting dynamic quantitative indexes of the metabolic load, and integrating the blood glucose fluctuation amplitude, insulin sensitivity and daily nutrition intake of the patient; constructing a metabolic load prediction model, and combining a time sequence deep learning algorithm with real-time data, including blood glucose, diet and exercise, of a patient to measure a future metabolic load; and establishing a causal relationship model between patient behaviors and metabolic reactions, and analyzing an interaction mode of diet, exercise and metabolic loads by adopting a causal inference technology.

The invention discloses a sea cucumber protein heptapeptide with functions of reducing blood pressure and blood sugar and application of the sea cucumber protein heptapeptide, and belongs to the technical field of bioactive peptide biology. The amino acid sequence of the sea cucumber protein heptapeptide is LPPGPFP. The sea cucumber protein heptapeptide can significantly inhibit the activity of ACE through a competitive inhibition mode, tolerates the gastrointestinal tract digestion environment, also has DPP-IV inhibition activity, can relieve hypertension and type 2 diabetes mellitus at the same time, and has an application value in preparation of products with the efficacy of reducing blood pressure and blood sugar due to multiple functions of the sea cucumber protein heptapeptide. The sea cucumber protein heptapeptide provided by the invention can significantly inhibit the activity of ACE through a competitive inhibition mode, has DPP-IV inhibition activity, has the characteristics of simple structure, safety, strong activity and the like, and has a wide application prospect.

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 disease prediction, in particular to an intelligent risk prediction method based on a neural network, and the method comprises the steps: collecting blood glucose and body mass index data, carrying out the clustering analysis, and constructing a multi-risk group feature; a risk transfer track before attack is recognized by combining a historical index change path and time sequence analysis; and calculating an individual risk coefficient by using deep learning, and comparing the individual risk coefficient with a risk threshold to generate early warning information, thereby realizing dynamic accurate evaluation. According to the method, by collecting the blood glucose level and body weight index data and conducting standardization processing, risk group division based on health index clustering can be achieved, a group recognition system with health feature differentiation is constructed, and the structured understanding of the individual health trend is enhanced. And on the basis of the clustered group, extracting a fluctuation path of individual historical indexes by means of a time sequence mode, identifying key transfer characteristics before disease attack, and enhancing the traceability of the disease formation process.

The embodiment of the invention provides a blood glucose monitoring and early warning method, device and equipment and a storage medium. The method comprises the following steps: acquiring historical blood glucose data and body index data of a user; determining a mean value and a standard deviation of the historical blood glucose data, and determining a blood glucose threshold value of the user based on the mean value and the standard deviation; key risk factors in the body index data and the weight corresponding to each key risk factor are determined, and the blood glucose risk score of the user is determined according to the key risk factors and the corresponding weights; and determining a blood glucose threshold value of the user and a target early warning level corresponding to the blood glucose risk score from a plurality of preset early warning levels, and executing a corresponding target early warning strategy in response to the condition that the target early warning level is greater than the preset early warning threshold value. The embodiment of the invention aims to improve the accuracy and flexibility of blood glucose monitoring and early warning.

The invention provides a multi-source heterogeneous data integrated coronary heart disease accurate prediction method, which comprises the following steps: acquiring various physiological signals of a patient in real time, the physiological signals comprising ST segment change characteristics and basic cycle function parameters in electrocardiosignals, and obtaining a multi-source signal data set; according to the joint data set, analyzing instantaneous fluctuation characteristics of a vascular resistance index in a postprandial hyperlipemia window period, extracting a vascular resistance fluctuation amplitude from the instantaneous fluctuation characteristics, and if it is detected that the fluctuation amplitude exceeds a preset threshold range, marking the window as a high-risk time window; if the score value exceeds a preset threshold value, triggering a coronary heart disease early warning signal according to the comprehensive risk score value in combination with a feature mode of a coronary heart disease hidden period in historical data; and storing the current high-risk time window characteristics and the blood sugar and blood fat curve change trend through a triggered coronary heart disease early warning signal to obtain structured risk archive data.

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 provides a diabetic neuropathy dynamic early warning method based on a multi-modal biological signal, and the method comprises the steps: integrating a multi-modal sensor through an intelligent wearable device, and constructing a personalized prediction model in combination with a deep learning algorithm; through an artificial intelligence algorithm, diabetic neuropathy (DN) development risk prediction and unperceptual hypoglycemia real-time early warning are realized. According to the time-frequency domain characteristics and clinical data of the multi-mode signal, the time window of the DN from the progress to the serious stage can be predicted; recognizing an unperceptual hypoglycemia risk by combining ECG and HR features with a blood glucose trend prediction model, and triggering grading intervention measures; based on the individual physiological response mode of the patient, the algorithm threshold is dynamically optimized, and the accuracy of prediction and early warning is improved. According to the method, HR, BP, ECG and other multi-dimensional signals are integrated, and the prediction precision is improved; continuous monitoring and instant feedback are supported; the self-adaptive algorithm adapts to individual differences of patients, and the limitation of a traditional general model is overcome.

The invention relates to the technical field of medical equipment, in particular to application equipment for treating diabetes mellitus, which is good in use effect and comprises a shell and a placing shell. By arranging an electric telescopic rod, a movable plate, a detection needle and a blood glucose detector, before insulin injection, the movable plate is pulled through the electric telescopic rod, at the moment, a vertical rod moves in a triangular groove, the detection needle is inserted into the skin of a patient, then the blood of the patient is detected, and the blood glucose detector is used for detecting the insulin. The real-time blood glucose condition of the patient can be detected, and the blood glucose detection function of the patient is achieved; by arranging an electric telescopic rod and a movable plate, when the device is not used, the movable plate is moved to the center position opposite to the fixed plate through the electric telescopic rod, at the moment, a treatment needle and a detection needle are both located in an inner cavity of the shell, discomfort caused when a needle head is inserted into the skin for a long time is avoided, and the use effect is good. The device has the advantages of being good in use effect and diversified in function.

The invention discloses a glucose response type insulin delivery system as well as a preparation method and application thereof, and belongs to the technical field of biological materials. The glucose response type insulin delivery system provided by the invention comprises a delivery carrier and a bioactive component encapsulated in the carrier, the delivery carrier is a metal polyphenol network formed by carrying out coordination reaction on epigallocatechin gallate and metal ions; the bioactive components comprise glucose oxidase, catalase and insulin. The glucose response type insulin delivery system provided by the invention can adjust the release of insulin according to the concentration of glucose in the surrounding environment, so that the blood glucose is maintained at a normal level.

The invention discloses an ear-nose-throat department symptom monitoring method and system based on multi-modal data fusion, and relates to the technical field of medical monitoring. Comprising the steps of monitoring physiological data in real time, calling historical medical record data, collecting environment data, and preprocessing the monitored, called and collected data; carrying out feature fusion on the preprocessed data, extracting potential associated features, and carrying out multilevel and sequential feature learning; on the basis of the extracted features, an improved long-short-term memory network is used for conducting symptom prediction, in combination with historical trends of the blood sugar and the blood pressure of the patient, correlation between the symptoms and ear-nose-throat symptoms is evaluated in real time, and personalized symptom early warning is output; and sending a health prompt to the patient through the intelligent equipment or the mobile terminal application in real time. According to the invention, conventional symptom early warning can be carried out, and personalized health intervention measures can be provided according to individual health conditions.

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 an exosome, a mesenchymal stem cell and application of the exosome and the mesenchymal stem cell in treatment of type 2 diabetes mellitus, and belongs to the technical field of biological medicine. The exosome secreted by the umbilical cord mesenchymal stem cells of the composite pretreatment group is used for treating the type 2 diabetes mellitus, and the obvious advantages are shown. Compared with a model control group, the exosome has the advantages that the glucose tolerance of diabetic rats treated by the exosome is obviously improved, the blood glucose value is reduced by about 47.5% within 120 minutes, the area under a blood glucose curve (AUC) is reduced by about 50.5%, the serum insulin is increased by about 147.7%, insulin secretion is effectively promoted, the glycosylated hemoglobin level is obviously reduced, and the long-term blood glucose control effect is good. By integrating various indexes, the exosome obtained by the composite pretreatment scheme provided by the invention has a remarkable effect of improving the illness state of rats with type 2 diabetes mellitus, provides a new effective strategy for treatment of type 2 diabetes mellitus, and shows a good application prospect.

The invention discloses a blood glucose concentration prediction method combined with a neural network, and relates to the technical field of blood glucose concentration prediction.The method comprises the steps that continuous blood glucose monitoring equipment interacts with a target user to conduct blood glucose monitoring, and a dynamic blood glucose concentration sequence in a preset monitoring window is extracted; acquiring an exercise data set, a food intake data set, an insulin use record set and an environment monitoring data set of the target user in the preset monitoring window; obtaining a blood glucose data topological framework sequence; obtaining a blood glucose feature recognition model; performing cross-scale mapping interaction analysis to obtain blood glucose associated features; training a blood glucose concentration prediction network layer; and performing blood sugar concentration prediction on the blood sugar related characteristics to obtain a blood sugar concentration prediction result. The technical problem that in the prior art, blood glucose concentration prediction is conducted in a single dimension, and the accuracy of the prediction result is low is solved, and the technical effect of improving the accuracy and timeliness of blood glucose concentration prediction is achieved.

The invention provides a health management method with a health data analysis function. The health management method with the health data analysis function comprises the following steps: S1, collecting health data information of a user body through existing equipment, and establishing a personal health record according to the data information, wherein the body data information comprises but is not limited to blood pressure, heart rhythm and blood sugar; s2, the collected health data need to be cleaned, sorted and preprocessed before a personal health record is established, so that subsequent data analysis is facilitated; and S3, according to the health data of the user, performing health risk assessment by using a machine learning model and a deep learning model algorithm, and predicting possible health risks, including but not limited to cardiovascular diseases and diabetes, of the user. The health management method with the health data analysis function provided by the invention has the advantages that the accuracy and individuation level of health management can be improved, and the requirements of people on health are met.

The invention discloses a measurement method, system and related device, the method is applied to first electronic equipment, the first electronic equipment comprises a first electrode group and a second electrode group, and the distance between the first electrode group and the second electrode group is greater than a first distance; under the condition that a first electrode group and a second electrode group are implanted into subcutaneous tissue, the method comprises the following steps: determining a first physiological parameter through the first electrode group; electrocardiosignals are determined through the first electrode set and the second electrode set. Therefore, the electrocardiosignal of the user can be obtained anytime and anywhere through the first electronic equipment, and one or more other physiological parameters (such as blood glucose, blood ketone, blood lactic acid and uric acid) of the user can be measured at the same time, so that the health monitoring efficiency is improved.

The invention discloses a blood glucose management method, a user interface and a related device, and the method can determine a first meal time based on a historical blood glucose value of a user, and determine a second meal time based on one or more of the meal time input by the user, the limb movement of the user and the heart rate. Third meal time is determined based on the first meal time and the second meal time, and the blood glucose health condition of the user is evaluated according to the third meal time. Therefore, according to the method, the dining time of the user can be predicted in combination with the dining time, determined in various modes, of the user in the historical diet event, and the electronic equipment can evaluate the blood glucose condition of the user more accurately.

The invention discloses a diabetic complication monitoring auxiliary system and method in the technical field of complication monitoring, and the system comprises a data collection module which is used for obtaining local multi-modal data of a user in real time; the federal learning module is used for integrating the local multi-modal data of the user with other anonymous medical databases through a distributed architecture and constructing a multi-family cross-institution complication prediction model; the causal reasoning module is used for analyzing the causal relationship among the blood glucose fluctuation, the glycosylated hemoglobin and the target complication based on an anti-factual causal reasoning algorithm, and outputting a causal effect value to correct the prediction deviation; the prediction module is used for generating occurrence probabilities of future specific complications based on the causal effect values and risk scores output by the complication prediction model, and classifying the occurrence probabilities according to risk levels; and the early warning module is used for generating personalized intervention suggestions according to the risk levels. According to the scheme, the data leakage risk in the data acquisition process is reduced through the federal learning distributed architecture, and the prediction reliability is improved.

The invention relates to an extraction electrode voltage determining method, device and system, a continuous blood glucose monitoring device comprises an extraction electrode, the extraction electrode is attached to the skin surface of a user, and the method comprises the steps that first loading voltage of the extraction electrode is obtained; under the condition that the extraction electrode is loaded with the first loading voltage, the current value of first current flowing through the skin surface of the user is determined; and when the current value of the first current is within a preset current value range, determining the first loading voltage as the loading voltage for an extraction electrode to extract the tissue fluid of the corresponding user.