75 results about "Diabetes management" patented technology

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 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 relates to the technical field of cloud data synchronization, in particular to a diabetes management-oriented cloud data synchronization method, which comprises the following steps: based on a blood glucose monitoring record, unifying a time zone and verifying a device serial number, generating a terminal version identifier, comparing a cloud version to screen missing entries, and identifying time abnormality and a device source. And forming a version difference mark, judging a missing range and a conflict identifier, updating a synchronization state after sorting, and counting the number of synchronization entries. According to the method, the problems of additional recording of historical data and version conflicts in data synchronization are effectively solved by introducing a dynamic rollback mechanism and timestamp difference comparison, clock differences between devices are automatically detected and adjusted in the synchronization process, timely updating and consistency of data are ensured, invalid synchronization operations are reduced through an intelligent conflict resolution mechanism, and the synchronization efficiency is improved. The accuracy and timeliness of data in diabetes management are ensured, and the data stability and consistency in a multi-terminal environment are improved.

The invention is an AI-powered fitness and health management system providing personalized, dynamic recommendations tailored to user-specific goals such as weight loss, muscle gain, and diabetes management. Leveraging Artificial Neural Networks (ANN), Decision Tree algorithms (J48), and Convolutional Neural Networks (CNN), the system analyzes user inputs, including voice commands, wearable device data, blood glucose readings, and food scans, to deliver real-time, adaptive meal and workout plans. Voice activation, initiated with the command “Run Fitzy”, enables hands-free logging of meals, workouts, and health metrics. Computer vision technology scans food labels and meal images, extracting nutritional data and automatically integrating it into the user's profile. The system dynamically adjusts remaining daily meals and activities to meet caloric, fitness, and glucose targets. Social networking features include geo-tagging, fitness challenges, and monetizable content sharing. Built on a secure, scalable cloud infrastructure, the platform integrates e-commerce functionality and ensures compliance with data protection standards.

A diabetes management system adapted to determine adherence for a subject in treatment according to a basal insulin regimen, the system being adapted to receive regimen data. BG data and insulin injection data. If one or more insulin injections have not been received in accordance with the prescribed regimen and thus are missing, the system is adapted to calculate for each missing injection an expected dose response BG value. By comparing received BG data, corresponding to the missing insulin injections, it can be determined for a given confidence interval whether or not the subject has been in adherence with the basal insulin regimen.

A mathematical model of type 1 diabetes (T1D) patient decision-making can be used to simulate, in silico, realistic glucose / insulin dynamics, for several days, in a variety of subjects who take therapeutic actions (e.g. insulin dosing) driven by either self-monitoring blood glucose (SMBG) or continuous glucose monitoring (CGM). The decision-making (DM) model can simulate real-life situations and everyday patient behaviors. Accurate submodels of SMBG and CGM measurement errors are incorporated in the comprehensive DM model. The DM model accounts for common errors the patients are used to doing in their diabetes management, such as miscalculations of meal carbohydrate content, early / delayed insulin administrations and missed insulin boluses. The DM model can be used to assess in silico if / when CGM can safely substitute SMBG in T1D management, to develop and test guidelines for CGM driven insulin dosing, to optimize and individualize off-line insulin therapies and to develop and test decision support systems.

The invention discloses a blood glucose data analysis system and method based on artificial intelligence, relates to the technical field of diabetes management, and aims to solve the problems of blood glucose data distortion and poor blood glucose monitoring suitability of gestational diabetes patients and children diabetes patients in a motion scene. The method comprises the following steps: acquiring blood glucose original data, exercise intensity data, skin impedance data, blood perfusion index data and special crowd exclusive physiological data, preprocessing, calculating three types of interference weight factors, substituting into a calibration formula to obtain corrected blood glucose data, generating a structured report, updating the calibration formula according to a period, and optimizing a special crowd model. By means of the method, motion multi-dimensional interference can be eliminated, the small sample modeling problem of special crowds is solved, and the blood glucose data precision and clinical practicability are improved.

The invention discloses a sweat induction and multi-parameter detection integrated sensor and a preparation method and application thereof, and belongs to the technical field of flexible biosensing, a PDMS flexible substrate is adopted to construct a multi-layer integrated structure, the sweat induction and multi-parameter detection integrated sensor comprises a sweat guide layer, a sweat collection layer, a detection electrode layer and a micro-fluidic chip layer, sweat secretion is promoted by accurately controlling the temperature to be less than or equal to 40 DEG C; the detection electrode layer adopts a silk-screen printing process to prepare a three-electrode system, comprises a specifically modified glucose working electrode and a beta-hydroxybutyric acid working electrode and can synchronously detect the concentration of glucose and beta-hydroxybutyric acid in sweat, and the micro-fluidic chip layer is integrated with a Bluetooth transmission module to realize real-time wireless transmission of detection data to a mobile terminal; the biosensor has the advantages of being good in biocompatibility, high in detection sensitivity and comfortable to wear and can tolerate bending with the curvature radius larger than 5 mm, and a non-invasive and continuous metabolism monitoring solution is provided for diabetes management.

The invention discloses a chiral precious metal nanocage induced and synthesized by penicillamine and a preparation method and application thereof, precious metals in the chiral precious metal nanocage are palladium, platinum and gold, the molar ratio of palladium to platinum to gold is (16-18): (24-28): (56-59), and the chiral precious metal nanocage has three-dimensional chiral morphology characteristics induced by penicillamine chiral molecules. And the edge length is in a nanometer size. Chiral deposition growth of gold atoms in two palladium-platinum nanometer hollow frame structures (cubes and icosahedrons) is induced through chiral penicillamine molecules (D- / L-penicillamine), and a chiral precious metal palladium-platinum-gold three-element hollow nanometer cage structure is constructed. The structure shows excellent catalytic activity on selective catalytic oxidation of D-configuration glucose molecules. The invention provides an efficient, low-cost and high-reliability biomimetic catalytic material solution for glucose detection, and promotes the upgrading of diabetes management, biosensing and food quality control technologies.

The invention provides a personalized diet recommendation method for pregnancy diabetes management, and the method comprises the steps: processing diet option data inputted by a patient through an optimized blood glucose response model, and generating an alternative diet recommendation list if the carbohydrate content in the diet option data causes a predicted blood glucose fluctuation trend to exceed a safety range, determining a personalized diet scheme; analyzing the matching degree of the historical blood glucose fluctuation sequence and the personalized dietary scheme according to an iterative blood glucose reaction model, and if the matching degree is judged to be lower than a preset threshold value, re-classifying interaction modes among dynamic factors to obtain refined blood glucose influence weight analysis; and integrating multi-dimensional data from the obtained refined blood sugar influence weight analysis to generate final real-time personalized diet recommendation, and if it is judged that the final implementation of the personalized diet recommendation can stabilize the abnormal blood sugar fluctuation during pregnancy, outputting a complete diabetes management guidance sequence.

The invention provides a diabetes abnormal index early warning system and method based on a recurrent neural network. The system comprises a data acquisition module which is responsible for collecting various health data including blood sugar, heart rate, blood pressure, body weight, body fat, exercise amount, sleep quality and heart rate variability from health monitoring equipment of a patient; the data preprocessing module is used for denoising and normalizing the collected health data and detecting and correcting abnormal values; the feature extraction module is used for extracting time sequence features, statistical features and principal component features from the preprocessed health data; the convolutional gating recurrent neural network model module is used for analyzing the extracted features; and the early warning generation module generates early warning information according to the prediction result. Through the system and the method, the real-time monitoring of the multi-dimensional health data of the diabetic patient and the early warning of the abnormal indexes can be realized, and the diabetes management effect and the life quality of the patient can be improved.

The application discloses a guidance text generation method and system based on diabetes management strategy recommendation. The method performs entity recognition on each text to obtain multiple entities, and extracts the relationship between the entities from each text. A knowledge graph is constructed according to the multiple entities and the relationship between the entities. A multi-layer attention layer is used to aggregate the entities and the relationship in the knowledge graph to obtain a first embedding representation. Text embedding of the entities and the relationship in the knowledge graph is generated, and a multi-layer attention layer is used to aggregate the text embedding into a second embedding representation. The first embedding representation and the second embedding representation are aligned and fused to obtain a target embedding representation. According to the target embedding representation and a target problem, multiple management strategy candidate vectors are determined. A target management strategy is selected from the multiple management strategy candidate vectors, and a diabetes management guidance text is generated according to the target management strategy. The application can improve the real-time performance, personalization and intelligentization of diabetes management.

Adaptive on board estimation of exogenous pharmacon responsive to transient (i.e., impermanent) physiological effects is provided. Dynamically estimating an equivalent amount of an exogenous pharmacon on board (XOB), such as insulin and / or carbohydrates, left in the subject, is based on predictions of glucose time-series data. These estimated values, such as insulin on board (IOB), are useful for diabetes management software, including decision support and / or artificial pancreas (AP) algorithms, for example.

A method for generating a diabetes management parameter user interface, the method comprising providing stored program instructions of a first diabetes management application (DMA), the first DMA configured to be stored in a memory of an electronic device and, when executed by a processor, the first DMA configured to: generate a first output display of a plurality of diabetes management parameters corresponding to a time series of analyte data, the plurality of diabetes management parameters depicting trends in diabetes management parameter levels over time; generate a graphical indicator of a link to a second DMA stored in the memory; and activate the second DMA in response to an input selection of the link, wherein the second DMA is configured to be executed by the processor to generate a second output display of current time diabetes management parameters corresponding to the time series of analyte data stored in the memory.

A diabetes management system optimizes glucoregulatory hormone delivery using a connected glucoregulatory hormone delivery system and a computing device with a processor and non-transitory memory. The system receives glucose management data (glucose, insulin delivery, heart rate, or carbohydrate consumption data) from sensors. A digital twin metabolic model is selected and initialized based on the received data. The system simulates diabetes treatment interventions to predict glucose dynamics and determines an optimized glucoregulatory hormone parameter (e.g., insulin dose or increment that minimizes hyperglycemia and hypoglycemia risk). The determined glucoregulatory hormone parameter is transmitted to the connected glucoregulatory hormone delivery system, which may include an automated insulin delivery (AID) system or a connected insulin pen system, for implementation. By integrating real-time physiological data with predictive metabolic modeling, the system enhances glucoregulatory hormone therapy, providing personalized adjustments to improve glucose control.

An insulin delivery system for diabetes management includes a patch-based insulin pump configured to deliver insulin to a user, a user input button disposed on the patch-based insulin pump, a processor configured to execute a hybrid closed loop algorithm that automatically controls insulin delivery based on glucose levels. The user input button is configured to receive user input comprising a sequence of button presses that communicate contextual information about user activities to the hybrid closed loop algorithm, the contextual information being distinct from direct insulin bolus delivery commands. The contextual information may include meal intake, physical activity, and sleep status communicated through different sequences of button presses. The system includes confirmation mechanisms that provide feedback and allow cancellation of inputs, enabling discrete real-time communication of personalized data to enhance diabetes management.

The invention discloses a diabetes health management system and method based on an autonomous evolution large model and a program product, and the system comprises a multi-modal data collection and preprocessing module which is used for collecting and preprocessing multi-modal data, related to diabetes, of a patient; the multi-modal embedding and feature fusion module is used for uniformly embedding and coding the multi-modal data and aligning and fusing the multi-modal data into a fusion feature; the health management suggestion generation module is used for generating personalized health management suggestions based on the fusion features; the evolutionary learning module is used for evoluting the health management suggestion generation module based on a reinforcement learning model; and the credible explanation and visualization module is used for providing basis traceability and trend display of the health management suggestions. According to the method, multi-modal modeling and self-adaptive learning are realized, and the interpretability is high.

This invention discloses a low glycemic index (GI) Hericium erinaceus (monkey head mushroom) steamed bun, belonging to the field of food processing and functional food applications. By weight, it is prepared from the following raw materials: 45-55 parts all-purpose flour, 28-35 parts Hericium erinaceus extract powder, 0.3-0.35 parts yeast, 15-18 parts water, 0.3-0.6 parts oil, and 0.7-0.9 parts sugar. This invention partially replaces the flour with Hericium erinaceus extract powder, fully utilizing the multiple mechanisms of action of Hericium erinaceus polysaccharides in forming a viscous gel in the digestive tract to slow down the contact between amylase and substrate, inhibiting α-amylase and α-glucosidase activity, and improving intestinal microecology and enhancing insulin sensitivity through dietary fiber. This allows the product to meet the low-GI food standard (GI<55), effectively filling the gap in low-GI steamed buns with Hericium erinaceus functional components as the core active ingredient. It has significant application value in the fields of diabetes management, weight control, and healthy staple foods.

A diabetes management system optimizes glucoregulatory hormone delivery using a connected glucoregulatory hormone delivery system and a computing device with a processor and non-transitory memory. The system receives glucose management data (glucose, insulin delivery, heart rate, or carbohydrate consumption data) from sensors. A digital twin metabolic model is selected and initialized based on the received data. The system simulates diabetes treatment interventions to predict glucose dynamics and determines an optimized glucoregulatory hormone parameter (e.g., insulin dose or increment that minimizes hyperglycemia and hypoglycemia risk). The determined glucoregulatory hormone parameter is transmitted to the connected glucoregulatory hormone delivery system, which may include an automated insulin delivery (AID) system or a connected insulin pen system, for implementation. By integrating real-time physiological data with predictive metabolic modeling, the system enhances glucoregulatory hormone therapy, providing personalized adjustments to improve glucose control.

Embodiments of the current disclosure are directed toward systems, devices and methods for diabetes management. In particular, the present disclosure relates to systems, devices and methods for dispensing insulin to a patient using a miniaturized and portable patch pump.

Embodiments of the invention provide amperometric analyte sensors having elements selected to optimize enzymatic activities associated with such sensors including polymers functionalized with enzymatic mediators as well as methods for making and using such sensors. While embodiments of the invention can be used in a variety of contexts, typical embodiments of the invention include glucose or ketone sensors used in the management of diabetes.

The invention discloses a guide text generation method and system based on diabetes management strategy recommendation, and the method comprises the steps: carrying out the entity recognition of each text, obtaining a plurality of entities, and extracting the relation between the entities from each text; constructing a knowledge graph according to the relationship between the plurality of entities; aggregating entities and relationships in the knowledge graph by adopting a plurality of attention layers to obtain a first embedded representation; generating text embedding for entities and relationships in the knowledge graph, and aggregating the text embedding into a second embedding representation by adopting a plurality of attention layers; aligning and fusing the first embedded representation and the second embedded representation to obtain a target embedded representation; determining a plurality of management strategy candidate vectors according to the target embedding representation and the target problem; and selecting a target management strategy from the plurality of management strategy candidate vectors, and generating a diabetes management guidance text according to the target management strategy. The real-time performance, individuation and intelligence of diabetes management can be improved.

Embodiments of the invention provide amperometric analyte sensors having optimized elements such as dexamethasone rejection membranes as well as methods for making and using such sensors. The amperometric analyte sensor apparatus comprises: a base layer; a conductive layer disposed on the base layer and comprising a working electrode; an dexamethasone rejection membrane disposed over an electroactive surface of the working electrode, wherein the interference rejection membrane comprises a poly Hema composition and an analyte sensing layer. While embodiments of the innovation can be used in a variety of contexts, typical embodiments of the invention include glucose sensors used in the management of diabetes.

The invention discloses an account association method for a diabetes management system, which comprises the following steps: searching information of a patient account in other accounts, if the patient account does not exist in a patient list of other accounts, sending an account association application to the patient account, and after the patient account receives the account association application, confirming the account association application; according to the method and the system, the association with the patient account can be realized, the patient only needs to confirm the association application, and other personnel do not need to skip among different platforms, so that the association between other accounts and the patient account is realized through a simple operation mode.

Diabetes management systems may include a glucose monitoring device adapted to transmit glucose data and at least one accessory in communication with the glucose monitoring device and adapted to provide at least one alarm or alert based on the glucose data.

1.The name of the design product: graphical user interface of a diabetes child blood glucose management system of an electronic device. 2.The use of the design product: the design product is an electronic device. 3.The design points of the design product: the interface content of the graphical user interface in the screen. 4.The picture or photo that best indicates the design points: interface change state diagram 1. 5.The electronic device is a conventional design, and the rear view, left view, right view, top view, and bottom view are omitted. 6.The use of the graphical user interface: the design product is used to provide child diabetes management services for users. 7.The human-computer interaction mode of the graphical user interface: after entering the mobile phone number, password, and verification code in the main view, clicking the “login” button, displaying interface change state diagram 1; clicking the “custom AI recipe” button in interface change state diagram 1, displaying interface change state diagram 2; clicking the corresponding recipe picture in interface change state diagram 2, displaying interface change state diagram 3.

A method for increasing functional and organoleptic properties of compositions having elements of biological origin more particularly herbal formulations is disclosed. Nutraceutical formulations for diabetes management and fortification of pulses and grains to lower oil absorption during frying are also disclosed. These foods are effective for weight management.

Ambulatory infusion pump systems can integrate a CGM sensor with an ambulatory infusion pump via a pump holder or tray that releasably holds the pump and is configured to be worn by a user. A direct electrical connection between the pump and the CGM can be facilitated by providing a sensor nest within the pump holder that causes an electrical connector on the pump to directly interface with the sensor. By combining the CGM and insulin pump into one integrated system with a direct electrical connection, a more aesthetically pleasing, convenient and reliable system is provided. Diabetes management is simplified by consolidating two essential devices into one.

The invention discloses a diabetes mellitus personalized nursing intelligent medical management system based on an AI algorithm, and belongs to the crossing field of intelligent medical treatment and artificial intelligence. According to the system, a three-layer dynamic coupling architecture of data perception, AI decision making and interactive execution is adopted, algorithms such as a space-time diagram convolutional network, hierarchical reinforcement learning and differential privacy federated learning are fused, and multi-source data intelligent fusion, 72-hour complication prospective early warning, minute-level personalized nursing scheme generation and model adaptive evolution are achieved. Clinical verification shows that the system can remarkably reduce the glycosylated hemoglobin level of a patient, improve the blood glucose standard-reaching rate and the scheme execution rate, reduce diabetes related emergency treatment and relieve the workload of medical care, and is low in deployment cost, convenient to operate, high in privacy, suitable for management of various diabetes mellitus and capable of being expanded to other chronic diseases, such as diabetes mellitus, diabetes mellitus, diabetes mellitus and diabetes mellitus. The system is suitable for multiple scenes such as medical institutions, communities and families.

To better understand the impact of glycemic events, some entities have begun examining the relationship between the blood glucose level and another physiological parameter. However, this relationship can be difficult to understand. Introduced here are computer programs and associated computer-implemented techniques for discovering glycemic events in a series of data values representative of blood glucose measurements and then altering the measurement schedule of a sensor capable of generating measurements in a dimension other than blood glucose based on the glycemic events. By altering the measurement schedule of the sensor, a diabetes management platform can better understand how, if at all, dimensions other than blood glucose are related to the glycemic health state of a subject whose blood glucose level is being monitored.