587 results about "Diabetic patient" patented technology

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 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 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 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, 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.

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 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 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 discloses a data-driven early-diabetic future diabetes-related disease progress risk prediction model and a construction method thereof, and the method comprises the steps: collecting clinical index data, including age, gender, BMI, WHR, HOMA-IR, HDL-C, TG, SBP, DBP, SCR and ALT, of early-diabetic patients in a training and verification queue; using an unsupervised soft clustering method combining dimension reduction based on UMAP, graph clustering and a Gaussian mixture model to identify the phenotypic heterogeneity of the prediabetes mellitus; obtaining the probability of the individual phenotype characteristics, evaluating the association between the probability and the development risk of the future diabetes related diseases in the early stage of diabetes, and constructing a development risk prediction model of the future diabetes related diseases in the early stage of urine diseases; and performing model optimization and robustness verification in the verification queue. According to the method, the heterogeneity of the prediabetes mellitus can be effectively identified, and accurate risk stratification and personalized prevention are realized.

The invention discloses a 3D holographic image knowledge base interactive playing method for diabetic education, and the method comprises the steps: firstly obtaining patient data of a hospital HIS system and a wearable device, and carrying out the classification, preprocessing and data fusion, thereby obtaining an encrypted standardized data set; and then multi-dimensional feature groups are extracted, knowledge base education contents are matched, and a personalized scheme is generated. And carrying out structural processing on the scheme, constructing a 3D model and a scene resource library, rendering the scheme into a holographic image format, optimizing the scheme, and outputting a material set. And then configuring a calibration material set, scheduling and playing, realizing immersive learning, and generating interaction data and feedback information. And finally, evaluating a learning effect by combining clinical indexes, and dynamically updating and optimizing the knowledge base. By means of 3D holography and multi-mode interaction, intuition, interactivity and individuation of diabetic education are improved, and the self-management ability is enhanced.

The invention relates to a healthy diet index evaluation method and system based on a diabetic patient. The method comprises the steps that a multi-modal data set of a patient is obtained, the multi-modal data set comprises diet image data, continuous blood glucose monitoring data and medicine use parameters, and mixed food material component analysis processing is conducted on the diet image data to obtain layered nutrition quantification data; performing synergistic effect analysis according to the drug use parameters and the pharmacokinetic model to generate a nutrition absorption correction coefficient; performing time sequence alignment processing on the layered nutrition quantification data and the continuous blood glucose monitoring data, and establishing a blood glucose response prediction model; and generating a diet health assessment report according to the blood glucose response prediction model and the nutrition absorption correction coefficient. According to the method, through the synergistic effect of the multi-modal data, the blood glucose response prediction model and the drug absorption correction coefficient, the diet effect of the diabetic patient can be accurately evaluated, nutrient absorption is optimized, and the scientificity and individuation degree of diet health management of the diabetic patient are effectively improved.

The invention discloses a diabetes cognitive impairment method based on metabonomics analysis and prediction, and relates to the technical field of biological information, and the method comprises the following steps: S1, obtaining metabonomics data and immunomics data from a peripheral blood sample of a diabetic patient, extracting relevant time sequence data aiming at glucose metabolism, and calculating the glucose metabolism related time sequence data; processing the sequence data by adopting a time sequence analysis algorithm to obtain time sequence change characteristics; s2, constructing a cross-omics interaction network according to time sequence change characteristics, integrating an incidence relation between metabolite concentration and immune factor expression, and setting a dynamic interaction mode; according to the diabetes cognitive impairment method based on metabonomics analysis and prediction, through multi-omics data integration and dynamic network analysis, the precision and reliability of diabetes cognitive impairment mechanism analysis are remarkably improved, and a theoretical basis is provided for precise intervention.

The invention provides a GelMA microneedle patch loaded with chromogenic gold nano bipyramids and a preparation method thereof.The method comprises the steps that citric acid, chloroauric acid and a CTAC solution are mixed, then sodium borohydride is added, and a seed solution is obtained through stirring and standing; mixing chloroauric acid, silver nitrate, hydrochloric acid and CTAB (cetyltrimethyl ammonium bromide), and sequentially adding ascorbic acid and the seed solution to obtain a growth solution; carrying out centrifugation, morphology regulation and chemical etching purification to obtain a chromogenic gold nano bipyramid solution; the preparation method comprises the following steps: reacting gelatin with methacrylic anhydride in PBS, dialyzing and freeze-drying to obtain GelMA; dissolving GelMA in the gold nanoparticle bipyramid solution, and adding a photoinitiator LAP to obtain a mixed precursor solution; and the GelMA microneedle patch loaded with the gold nano bipyramid is prepared through mold casting, centrifugation, ultraviolet light crosslinking and drying demolding. The microneedle extraction and gold nano bipyramid color development integrated scheme is initiated, blood sampling is not needed, the dermis interstitial fluid is extracted in a minimally invasive mode through the microneedle, detection pain and trauma are reduced, the cross infection risk of blood treatment is avoided, and the microneedle microneedle microneedle microneedle microneedle microneedle microneedle microneedle microneedle microneedle microneedle microneedle microneedle microneedle microneedle microneedle microneedle microneedle microneedle microneedle microneedle microneedle microneedle microneedle

Diabetes is a disease characterized by high levels of blood glucose. It is important to keep diabetes under control to avoid short- and long-term complications. Diabetes can affect vision due to the alterations it produces in the blood vessels of the retina. This is known as Diabetic Retinopathy (DR), which is one of the leading causes of impaired vision in developed countries. One of the complications of diabetic retinopathy is Diabetic Macular Edema (DME), which is the leading cause of vision loss in diabetic patients and can appear at any stage of diabetic retinopathy. This consists of the gradual accumulation of fluid in the macula, the most important area of the retina. The determination of diabetic macular oedema is very important for the retina. The determination of diabetic macular oedema is very important for adequate treatment of this condition. A variety of technological options are used for detecting diabetic retinopathy, although only the most sophisticated detect macular oedema, a complication that appears as a consequence of diabetic retinopathy and is one of the leading causes of blindness. The invention describes a portable system for detecting diabetic macular oedema by capturing a fundus image using a portable ophthalmoscope; said image is sent via wired or wireless means to an embedded system that has an algorithm based on artificial intelligence, which extracts information from the image and processes same to identify the presence of the condition being studied.

The invention belongs to the technical field of biological medicines, and particularly relates to application of 1, 3-propane diamine or pharmaceutically acceptable salts thereof in preparation of medicines for preventing or treating diabetes. Experimental results show that 1, 3-propane diamine can obviously improve random blood glucose and fasting blood glucose of mice with type 2 diabetes mellitus, and 1, 3-propane diamine can obviously improve insulin resistance of model mice. According to the application, 1, 3-propane diamine is used for improving the blood sugar of patients with type 2 diabetes for the first time, an ideal treatment effect is achieved, a new treatment choice is provided for improving the blood sugar level through clinical application of 1, 3-propane diamine, and the application has extremely high application value and social benefits.

The invention provides an intelligent insulin closed-loop infusion system for type 1 diabetes mellitus, and relates to the technical field of diabetes mellitus treatment equipment. The system comprises a blood glucose monitoring module, a control module, an insulin infusion module, a user interaction module, a wireless communication module and a power module, and closed-loop precise infusion of insulin is achieved through cooperation of the multiple modules. Wherein a closed-loop algorithm based on a dynamic basic rate formula is arranged in the control module, and the insulin infusion amount can be dynamically adjusted in combination with an LSTM blood glucose prediction model and a metabolic parameter least square method updating unit; the insulin infusion module adopts a micro peristaltic pump controlled by PWM (Pulse Width Modulation) and a current monitoring type blockage detection unit, so that the infusion precision and safety are guaranteed. According to the system, the blood glucose standard reaching time proportion can be increased to 78%, the hypoglycemia occurrence frequency is reduced to 0.8 times per week, the system is remarkably superior to a traditional insulin pump and a simple closed-loop system, meanwhile, the system has the advantages of being long in sensor service life and low in power consumption, and the treatment convenience and safety of type 1 diabetes patients are improved.

The invention discloses a diabetic intestinal preparation quality prediction method based on SHAP, and relates to the technical field of medical artificial intelligence, and the method comprises the following steps: collecting clinical feature data of a diabetic, and carrying out the preprocessing of the clinical feature data; constructing a diabetic intestinal preparation quality prediction model, taking the preprocessed clinical feature data as model input, and taking the intestinal preparation qualification rate as output; and an SHAP interpretable artificial intelligence framework is introduced to analyze and attribut the prediction model. According to the method for predicting the intestinal preparation quality of the diabetic patient, the prediction model is constructed by integrating multi-dimensional clinical data and applying an ensemble learning algorithm, so that the prediction accuracy and stability are remarkably improved, the limitation that the traditional method depends on doctor experience is broken through, the judgment inconsistency caused by individual difference and diagnosis and treatment pressure is reduced, and the prediction accuracy and stability are improved. The interaction between the features is displayed through a visualization tool, and the interpretability and transparency of the model are enhanced.

The invention relates to the technical field of biological materials, in particular to a pH response collagen microneedle patch loaded with antibacterial nanoparticles as well as a preparation method and application of the pH response collagen microneedle patch loaded with the antibacterial nanoparticles, and the microneedle patch is prepared by loading the antibacterial nanoparticles, aldehyde modified F127 and recombinant three-type collagen COL3 on a polymer matrix. When the microneedle patch is applied to a chronic wound of a diabetic patient, the microneedle penetrates through the skin in a minimally invasive mode and directly reaches a wound focus area. Under the irradiation of 808nm near-infrared light, the mesoporous polydopamine absorbs light energy, so that the loaded lyase LysAB2 is quickly released, bacterial cell walls are specifically recognized and cracked, and bacteria are efficiently killed; meanwhile, the collagen hydrogel constructs an appropriate microenvironment at the wound, cell adhesion, proliferation and migration are promoted, and the wound healing process is accelerated. Compared with a traditional diabetes mellitus chronic bacterial infection wound treatment means, the microneedle patch provided by the invention provides an efficient and safe new strategy for treating the chronic bacterial infection wound of a diabetes mellitus patient.

The invention provides a diabetes chronic complication early warning method and device, a medium and computer equipment. The early warning method comprises the following steps: acquiring body data of a diabetic patient; screening the body data by adopting an LASSO logistic regression model to generate a data training set; training the multi-layer perception model by using the data training set to obtain a diabetic complication early warning model; obtaining to-be-detected body data, inputting the body data into the diabetic complication early warning model for prediction after screening, and generating an early warning result; by the adoption of the method, dimension reduction is conducted on the collected body data through the LASSO logistic regression model, the training data of the model can be effectively reduced, the computing power requirement for the training and reasoning process is lowered, the model can be conveniently deployed to a local computing end, and therefore the diabetic patient can conveniently achieve home detection. Compared with conventional blood detection, the non-invasive blood detection device has the advantages of being non-invasive and small in operation difficulty.

The present invention discloses modified-release bilayer tablets consisting of two layers, layer 1 consisting 500 mg sustained release metformin and layer 2 consisting 250 mg normal release metformin and 5mg / 10mg delayed-release dapagliflozin. This formulation is meticulously designed to regulate blood glucose levels effectively over an extended period. Immediate-release metformin swiftly addresses acute glucose spikes, while sustained-release metformin ensures prolonged glucose control, minimizing fluctuations throughout the day. The delayed-release dapagliflozin component allows for controlled and timed release, managing persistent hyperglycemia synergistically with metformin. By optimizing efficacy and minimizing glucose fluctuations, this innovative formulation offers a promising solution for stable glycemic control in individuals with diabetes. Present invention aims to improve patient outcomes and enhance overall quality of life by maintaining stable glucose levels and reducing the risk of complications associated with uncontrolled diabetes.

The invention discloses a diabetic complication risk assessment method based on unequal-length CGM data, belongs to the field of assessment methods, and aims to solve the problem of how to extract complication related features from unequal-length CGM sequences of diabetic patients and realize efficient risk assessment on the premise of not losing dynamic information as far as possible. The method comprises the following steps: step 1, acquiring a multi-day CGM data sequence of a diabetic patient; 2, performing feature extraction based on the multi-day CGM data sequence to obtain a multi-day CGM sequence feature C; and step 3, inputting the multi-day CGM sequence feature C into the FCNN to obtain the probability that the diabetic suffers from complications.

The invention relates to a pre-sleep hypoglycemia risk probability assessment method based on local-global features, and belongs to the technical field of medical risk assessment. According to the method, a feature set is constructed through the multi-source information domain time sequence data and the target domain time sequence data of the patient to reflect the blood glucose level of the patient; searching a common subspace with the minimum distribution difference through the heterogeneous domain adaptive model to obtain a target function, and calculating the target function to obtain a shared representation space; mapping the feature set into a shared representation space, and obtaining a representation sample set of projection from the shared representation space; and evaluating the pre-sleep hypoglycemia risk probability of the characterization sample set through a transfer learning method based on local-global features. According to the method, the performance of an early warning system can be effectively improved, and better guarantee is provided for early warning of hypoglycemia at night before sleep of diabetic patients.

The invention discloses a system and method for deducing diabetes based on an error value detection and correction algorithm, and the system comprises a data collection and integration module which is used for collecting multi-source data corresponding to a diabetic patient, and carrying out the format unification and arrangement of the multi-source data; the error value preliminary screening module is used for preliminarily screening abnormal data in the multi-source data; the depth error value correction module is used for correcting the preliminarily screened abnormal data based on a causal inference algorithm to obtain corrected data; the feature extraction and association module is used for extracting potential features related to diabetes in the corrected data and establishing an association model between the features; and the diabetes mellitus inference module is used for inputting the associated feature data into a pre-trained inference model for processing, and the inference model outputs the diabetes mellitus illness probability and related diagnosis suggestions.