55 results about "Diabetes risk" patented technology

The invention relates to the technical field of diabetes, in particular to a diabetes risk prediction model based on a multi-mode large model. The system comprises a multi-data fusion acquisition unit, a model construction selection unit and a user classification unit. According to gene detection and family history data, different models are allocated to predict the diabetes risk, it is ensured that the prediction result is closer to the actual situation of an individual, the family history data are combined, the individual risk can be evaluated more comprehensively, targeted health management suggestions are provided for family members, the family data are integrated into the models, and the risk of diabetes is predicted. The method is beneficial for discovering a family genetic mode and improving the prediction accuracy, and can improve the prediction accuracy through family history data even if no gene signal exists, so that the existing information is fully utilized.

The invention discloses a retina image unsupervised anomaly detection method for early screening of diabetes mellitus. The method comprises the following steps: carrying out registration and multi-scale attention-guided blood vessel segmentation on a longitudinal time sequence retina image of a patient; extracting a vascular skeleton and constructing a time sequence vascular topological graph, calculating geometric morphology and hemodynamic attributes of each vascular segment, identifying vascular morphology evolution characteristics by comparing topological graphs of adjacent time points, and calculating hemodynamic characteristics such as wall shear stress through simulation; the evolution and hemodynamic characteristics are jointly input into a time sequence encoder for unsupervised learning, and an early diabetes risk score is comprehensively generated by analyzing a reconstruction error, an abnormal score based on density estimation and a time sequence trajectory deviation degree of a potential space; the scheme of the invention does not depend on lesion labels, and can sensitively detect the tiny anomalies at the early stage of pathology from multi-dimensional dynamic changes, thereby providing an objective and quantitative new way for early screening and intervention of diabetes.

The invention provides a gestational diabetes risk prediction system and method based on multi-modal data fusion, and the system comprises a data collection module which is used for integrating clinical indexes and medical record text data; the data preprocessing module converts the multimode data into numerical values and text variables which can be used for modeling; the variable screening module is used for extracting data features by adopting LASSO regression in combination with a recursive feature elimination algorithm and a Clinical-BERT model; the data prediction module is used for constructing a GDM risk prediction model through a dual-channel calculation unit and a fusion unit, generating an accurate risk probability and providing an interpretable clinical index in combination with an SHAP value; and a prediction result is output through the output module in the forms of a dynamic column diagram, a webpage calculator and an API interface, so that clinical operation and application are facilitated. According to the method, the limitation of a traditional prediction method is broken through, the prediction precision and the real-time monitoring capability are improved, and the development of precise medical treatment is promoted.

The invention relates to the technical field of diabetes prediction, in particular to a method for predicting early diabetes based on health data, and the method comprises the steps: carrying out the data collection and preprocessing of an individual health system, and obtaining diabetes risk related data; performing off-line calculation on the data by using a feature correlation analysis technology, and extracting a diabetes prediction feature set; performing risk assessment on the feature set through a preset diabetes prediction model to generate a diabetes risk prediction result; key risk factors are extracted, a risk factor classification model based on a random forest is constructed, risk attribution analysis is carried out, and main risk factors are identified; then, comprehensive assessment is carried out on the main risk factors in combination with confidence propagation analysis and risk path analysis technologies, and a core risk source is positioned; the individual diabetes risk portrait is constructed according to the core risk source, the relationship among the risk factors is analyzed, a personalized early intervention scheme is formulated, and the comprehensiveness, precision and interpretability of early diabetes prediction are remarkably improved.

The invention relates to the technical field of medical data analysis and clinical risk prediction, provides a construction method of a glucocorticoid induced diabetes risk prediction model based on an LASSO algorithm, and belongs to the technical scheme of data processing executed on computing equipment. Clinical data of inpatients receiving systemic glucocorticoid treatment are collected, key variables are screened through LASSO regression, and a Logistic regression model is constructed to achieve risk prediction. The model is composed of four conventional clinical indexes, has good distinction degree and calibration degree, and finally realizes individualized risk assessment through column diagram form output. The method is simple, practical and accurate, and is suitable for SDM prediction and intervention management of clinical high-risk groups.

The invention discloses a diabetes prediction and interpretability analysis method and a computer program product, and belongs to the technical field of medical information.The diabetes prediction and interpretability analysis method comprises the steps that a sample data set is constructed; constructing a mathematical model of a feature self-attention module; constructing an improved residual network model of a fusion feature self-attention module; performing diabetes risk prediction on the sample data by using the improved residual network model; calculating an SHAP value of each input feature vector by using an SHAP model, and evaluating marginal contribution of the input feature vectors to model output; and generating a visual abstract graph of the SHAP value, and displaying the specific influence of each feature on a prediction result. According to the method, the self-attention mechanism and the deep residual idea are fused, and the prediction performance of the RAC model is remarkably improved. By introducing SHAP feature analysis, the contribution of each feature to prediction is quantified, the interpretability of the model is enhanced, and systematic evaluation can be provided for diabetes prediction.

The invention provides a diabetes risk prediction method based on an attention-enhanced deep belief network. The diabetes risk prediction method comprises the steps of preprocessing collected original data; screening key features of diabetes by using a voting integrated feature selection method combining chi-square test, mutual information gain and variance threshold; generating synthetic data for minority class data in the screened key features by using a generative adversarial network GAN; performing attention mechanism weighting processing on the feature data to generate a weighted context vector, inputting the weighted context vector into a deep belief network, and outputting a diabetes disease probability; in the training process, the cross entropy loss and the focus loss are combined to form a mixed loss function which is used for guiding parameter adjustment of the DBN module and the attention module; and outputting a diabetes risk prediction result. According to the method, the defects in the aspects of highly unbalanced data processing, feature selection and importance, model structures and loss functions in the prior art can be overcome, and the accuracy and reliability of diabetes risk prediction are improved.

The invention discloses a hierarchical diabetes risk assessment method based on big data and deep learning, and belongs to the technical field of big data, and the method comprises the following steps: combining a one-dimensional convolutional neural network and an attention mechanism in deep learning, carrying out the hierarchical analysis of physical examination data in different scenes, and achieving the accuracy of diabetes risk assessment; a normal distribution mapping model is provided and is used for remapping a prediction result to standard normal distribution, so that risk grade division is realized; single sample feature importance sorting is carried out based on a data perturbation method, and personalized diabetes risk assessment is carried out for individual health conditions. A layered and scene-divided diabetes risk assessment system is constructed based on large-scale physical examination data in combination with artificial intelligence and a deep learning technology, and high-precision and personalized diabetes risk assessment can be provided according to different application scenes and individual health data, so that early warning and accurate health management are realized.

The invention discloses a method for identifying early-stage diabetic patients based on physiological and biochemical indexes. The method comprises the following steps: collecting data; analyzing and preprocessing data; a BP neural network model is adopted, and the model comprises an input layer, six hidden layers and an output layer; model training: dividing the source data into a training set and a test set according to the ratio of 7: 3 for model training, the training round epoch is 70, the batch size is 16, and in the training process, 10% of the training data is used as a verification set for evaluating the performance; and evaluating a result. By comprehensively considering the multi-dimensional clinical data of the patient, the classification model capable of accurately identifying the risk of the early diabetes mellitus of the individual is developed, and the accuracy and reliability of early diabetes mellitus identification are improved; and a doctor can be assisted to formulate personalized prevention and treatment strategies through the identification result of the model.

The embodiment of the invention discloses a diabetes risk prediction method and device, electronic equipment and a storage medium. The method comprises the following steps: determining a first metabolic diagram of a target object at a current moment according to metabolic data of the target object under a plurality of metabolic detection indexes at the current moment; wherein the first metabolic diagram comprises a plurality of metabolic nodes, and the metabolic nodes correspond to the metabolic detection indexes; inputting the first metabolic diagram into a pre-trained feature extraction model to obtain first node feature data of each metabolic node in the first metabolic diagram; and inputting the first node feature data into a pre-trained sub-risk prediction model to obtain a diabetes risk level of the target object at the next moment. According to the technical scheme provided by the embodiment of the invention, the risk level of the target object suffering from diabetes mellitus can be predicted more conveniently and accurately.

The invention discloses a diabetes risk prediction method and system based on multiple omics markers, and relates to the technical field of biomedical engineering, and the method comprises the steps: collecting multiple omics data of a plurality of basic time points and a plurality of metabolic stress trigger points of a subject; according to the method, a sampling mode of combining a basic time point and a metabolic stress trigger point is adopted, and dynamic network marker identification is matched, so that a disease early warning time window is greatly advanced, and the critical state change before the disease attack can be effectively captured; multiple omics data are integrated to construct a four-dimensional matrix, a subtype specificity module is mined in combination with a time sequence causal Bayesian network, and prediction comprehensiveness and subtype pertinence are improved; feature weights are dynamically distributed through reinforcement learning, and prediction precision and generalization ability are both considered in combination with a digital twinborn enhanced model architecture; the model architecture has clear biological mechanism association, outputs a multi-task prediction result, and provides an interpretable personalized basis for clinical intervention.

A device for measuring and monitoring diabetes, consisting of a system-on-chip for real-time processing of sensor data, several sensors connected to the system-on-chip, including a blood glucose sensor, a heart rate sensor and a blood pressure sensor, a machine learning model implemented on the system-on-chip for analyzing the sensor data and generating a diabetes risk score, and a memory buffer within the system-on-chip for storing sensor data for trend analysis and improved prediction accuracy.

1. Name of the designed product: Diabetes risk disk. 2. Use of the designed product: Diabetes risk disk. 3. Design points of the designed product: In shape. 4. Picture or photograph best indicating the design points: Perspective view 1.

The invention relates to the technical field of medical data processing, in particular to a multi-modal data fusion diabetes risk prediction system after acute pancreatitis, and the method comprises the steps: obtaining the feature data and treatment indexes of each dimension of a target patient and a plurality of reference patients after acute pancreatitis; obtaining a weighted similarity index according to the difference between the feature data of the target patient and the feature data of each reference patient in the same dimension; according to the treatment index of each reference patient and the distribution difference of the overall reference patients, a boundary feature length is obtained in combination with weighted similar indexes; fitting the treatment indexes of the reference patients whose diagnosis time lengths are greater than the demarcation characteristic length and less than or equal to the demarcation characteristic length respectively to obtain a pancreas islet compensation function curve; and obtaining a diabetes risk prediction result of the target patient according to a deviation condition between the treatment index of the target patient and the pancreas islet compensation function curve. According to the invention, the accuracy of the risk prediction result of the patient in the compensatory state is good.

The invention discloses an intelligent risk assessment system for diabetes mellitus based on intestinal flora, and relates to the technical field of diabetes mellitus detection.The intelligent risk assessment system comprises the steps that firstly, the content information of biochemical markers is obtained from urine samples of people meeting the requirements before detection; the data is preliminarily analyzed to screen out diabetes risk personnel and send out a prediction instruction, according to the risk prediction instruction, excrement samples of marked personnel are detected, the proportion of the number of related strains and the concentration information of short-chain fatty acid are analyzed, a risk assessment index Zpg is calculated in combination with biochemical marker data, and finally, the risk assessment index Zpg is calculated. According to the method, whether the current person suffers from diabetes or not is comprehensively judged by comparing the current person with a set evaluation threshold value P, and the whole process is subjected to multi-dimensional detection and analysis, so that the accuracy and effectiveness of early screening of diabetes mellitus are improved while the cost is reduced through rapid detection.

The present application relates to the technical field of digital medical treatment, in particular to a pregnancy-induced diabetes risk early warning method based on multi-modal data fusion. Multi-modal data such as blood glucose time series data, exercise amount, body weight and placenta thickness of pregnant women during pregnancy are acquired, the peak value and back-off characteristics of blood glucose time series are analyzed, the exercise amount is combined, the blood glucose back-off trend degree is quantified, and the dynamic metabolic control capability is accurately reflected; further, on the basis of placenta thickness data, the body weight change condition, the exercise amount and the blood glucose back-off trend degree are combined to determine the placenta thickness estimation value time series data, and the physiological correlation characteristics between various data are improved; finally, the blood glucose back-off trend degree mean value and the placenta thickness sustained increasing trend characteristics are fused to reveal the correlation characteristics of blood glucose and abnormal placenta development, a neural network model is trained to capture multi-factor nonlinear relationships, and early and accurate risk prediction of pregnancy-induced diabetes (GDM) is realized.

The invention relates to the field of artificial intelligence and medical health monitoring, in particular to a diabetes risk monitoring method and system based on artificial intelligence. The method comprises the following steps: acquiring original data of a user from a plurality of data sources, wherein the original data comprises physiological parameters, living habit data and family history case information; preprocessing the original data, including removing abnormal values and processing missing data; inputting the preprocessed original data to a deep LSTM-Cox model, and evaluating the diabetes risk of the user; outputting the diabetes basic occurrence probability of the user; and calculating an optimized comprehensive diabetes risk score, and dividing the users into different diabetes risk grades. According to the diabetes risk monitoring method and system based on artificial intelligence, local features and long-term trend features of health data can be extracted from different dimensions, comprehensive prediction of the diabetes risk is performed after the features are fused, and the diabetes risk of an individual can be evaluated more accurately.

The invention relates to the technical field of digital medical treatment, in particular to a gestational diabetes risk early warning method based on multi-modal data fusion. Blood sugar time sequence data, exercise amount, body weight, placenta thickness and other multi-modal data of the pregnant woman in the gestation period are obtained, the blood sugar falling tendency degree is quantified by analyzing the peak value and falling characteristics of the blood sugar time sequence and combining the exercise amount, and the dynamic metabolism control ability is accurately reflected; further, on the basis of the placenta thickness data, determining placenta thickness estimation value time sequence data in combination with the body weight change condition, the exercise amount and the blood glucose falling tendency degree, and improving physiological related characteristics among various data; and finally, fusing the blood glucose fall-back trend mean value and the placenta thickness continuous increasing trend characteristic, revealing the correlation characteristic of blood glucose and placenta abnormal development, and training a neural network model to capture a multi-factor nonlinear relationship, thereby realizing early and accurate risk prediction of gestational diabetes mellitus (GDM).

This invention discloses a method, device, and storage medium for predicting the risk of diabetes in patients with periodontal disease, relating to the field of smart healthcare technology. The method includes: acquiring biological samples from patients with periodontal disease; detecting the expression levels of core mediator proteins in the biological samples, including C1S, PSAP, and CFP; acquiring clinical covariate data from patients with periodontal disease; inputting the expression levels of the core mediator proteins and the clinical covariate data into a pre-trained diabetes risk prediction model to obtain a prediction of the risk of developing diabetes in patients with periodontal disease within a predetermined time window; wherein, the diabetes risk prediction model is based on the XGBoost algorithm, using the expression levels of the core mediator proteins and the clinical covariate data as input features, and using the outcome of diabetes as a label for training. This invention provides a solution that can systematically elucidate the causal relationship between periodontal disease and diabetes, achieving high-precision prediction of the future risk of developing diabetes in patients with periodontal disease.

This invention relates to the field of medical data processing technology, specifically to a multimodal data fusion system for predicting diabetes risk after acute pancreatitis. The system includes: acquiring feature data and treatment indices for each dimension of the target patient and several reference patients after acute pancreatitis; obtaining a weighted similarity index based on the differences in feature data between the target patient and each reference patient in the same dimension; obtaining a cutoff feature length based on the treatment index of each reference patient and the overall distribution differences of the reference patients, combined with the weighted similarity index; fitting the treatment indices of reference patients whose diagnosis time is greater than or less than or equal to the cutoff feature length to obtain pancreatic islet compensation function curves; and obtaining the diabetes risk prediction result for the target patient based on the deviation between the target patient's treatment index and the pancreatic islet compensation function curves. This invention shows better accuracy in predicting the risk of patients in a compensated state.

A diabetes risk detection method, an electronic device, and a system are disclosed and are applied to the field of terminal technologies, to resolve a problem in the conventional technology that an obtained diabetes risk detection result is inaccurate when time for collecting a photoplethysmography signal is short. The method includes: obtaining first data and second data, where the first data includes a photoplethysmography (PPG) signal obtained via a PPG sensor, and the second data includes one or more of the following: diet data, exercise data, physical symptom data, drug use data, sleep data, or emotion data; determining a diabetes risk detection result based on the first data and the second data; and outputting the diabetes risk detection result to prompt a user with a health status.

Present disclosure describes techniques for predicting diabetes risk in patients. The techniques include the step of monitoring a plurality of patient-specific characteristics comprising, at least one physiological parameter, one behavioral indicator, and one visual representation of the patient. The method further comprises extracting, using a first artificial intelligence (AI) model, a stress level of the patient based at least on behavioral indicators, historical lifestyle data, and sensor-derived physiological parameters. The method then include extracting, using a second AI model, a body mass index (BMI) or fat distribution patterns based at least on silhouette images and weight of the patient. The method finally includes predicting, using a third AI model, a blood sugar level or diabetes risk score of the patient based on outputs from the first and second AI models and the monitored characteristics.

The invention discloses a parameter optimization and prediction method of a diabetes risk prediction model and a related device, and relates to the field of artificial intelligence, and the method comprises the steps: taking a randomly generated initial population as an initial particle swarm, carrying out the at least one particle updating of the initial particle swarm through employing a particle swarm optimization algorithm based on reinforcement learning, and obtaining an updated particle swarm, forming a first population by at least part of particles in the updated particle swarm, performing model training on the first population by using a first training set to obtain a second population, if a genetic convergence condition is not met, performing individual updating on the second population by using a genetic algorithm based on reinforcement learning to obtain an updated population, and taking the updated population as an initial particle swarm, and returning to the particle updating step until a genetic convergence condition is met, and determining a final parameter combination from the second population. According to the method, reinforcement learning, the particle swarm optimization algorithm and the genetic algorithm are combined for parameter optimization, the optimization accuracy and efficiency are improved, and the model prediction performance is improved.

In an approach to estimating diabetes risk, the present invention receives a biomarker signal over a first period of time. From the biomarker signal, the presently claimed invention extracts one or more features of the biomarker signal and determines a first trend associated with insulin resistance based on the one or more features of the biomarker signal. Using this information, the present invention can determine a relative non-digestion-related contribution to the blood glucose signal and / or a relative digestion-related contribution to the blood glucose signal is determined based on the one or more features extracted from the blood glucose signal. In some cases, an absolute digestion related contribution and / or an absolute non-digestion- related contribution is determined from the relative digestion-related and non-digestion-related contribution.

The application discloses a kind of potential metabolic state modeling gestational diabetes risk assessment and intervention decision method, it is related to medical information technology field, including: first, obtain the minimum metabolic modeling data structure containing individual static information, follow-up observation and intervention data;Second, initialize the latent metabolic state vector formed by insulin sensitivity, beta cell compensatory capacity, fat burden and other latent variables;Then, an observation mapping and state updating mechanism is established, and the state vector is dynamically corrected and updated using follow-up observation and intervention data;Subsequently, based on the updated vector, a metabolic system stability index is calculated to assess patient risk stratification;Finally, by counterfactual reasoning, the state evolution under different hypothetical intervention scenarios is simulated, and the intervention priority and recommendations are output.The present application realizes early identification and precise intervention of risk through microscopic modeling and deduction of latent metabolic state, significantly improving the effectiveness of perinatal and postpartum health management.

The embodiment of the invention discloses a diabetes management hearing aid system and a diabetes monitoring method based on the same, and relates to the technical field of hearing aid devices and algorithms. The system comprises a hearing aid; the flexible sweat sensor patch comprises a sweat collecting hole, an electrochemical electrode and a micro-channel, the outer side of the flexible sweat sensor patch is used for being attached to the skin behind the ear, and the inner side of the flexible sweat sensor patch is attached to the outer surface of the hearing aid; sweat of the skin behind the ear flows into the micro-channel through the sweat collecting hole, the sweat in the micro-channel is subjected to electrochemical reaction under the action of the electrochemical electrode, an electric signal is generated and transmitted into the hearing-aid chip, and the hearing-aid chip detects the blood glucose concentration through an internally integrated analysis algorithm. And the disease monitoring system is used for recording the detection data, analyzing the diabetes risk and linking the hearing aid to carry out in-ear early warning when the data is abnormal. The system is noninvasive and portable, and can solve the problem of user hearing and diabetes management at the same time.

This invention discloses a diabetes complication risk prediction system based on disease prior masks, belonging to the field of diabetes risk assessment technology. By designing a disease relationship learning module based on pathology masks, irrational disease interactions are suppressed and inter-task information flow that violates medical logic is shielded. This forces the model to learn only clinically meaningful shared representations, reducing the risk of learning incorrect associations under noisy data and significantly enhancing the model's medical interpretability, making its internal decision-making mechanism highly consistent with clinical pathological mechanisms. Furthermore, the introduced relationship attention gating mechanism adaptively adjusts the fusion ratio between disease relationship information and task-specific features according to the needs of different complication tasks, enhancing beneficial relationship signals when necessary and automatically suppressing irrelevant features when noise is high. This achieves more stable and efficient multi-task feature interaction and collaborative learning, enabling more accurate prediction of diabetes complications.

The invention belongs to the technical field of diabetes risk prediction, and provides a method and system for predicting the diabetes risk of children prediabetic population, and the method comprises the following steps: S1, multi-modal data fusion collection: collecting children clinical data, biochemical indexes, dynamic blood glucose monitoring data and type 1 diabetes specific data; s2, feature engineering and model construction: carrying out feature engineering processing on the collected data, and extracting key indexes; s3, model training: performing risk prediction by using a machine learning model, and outputting a risk level and a confidence coefficient; s4, generating a personalized intervention scheme according to the risk level; according to the method, the accuracy and reliability of prediction are remarkably improved by combining comprehensive analysis of multi-modal data, key indexes can be accurately extracted through feature engineering and model training, risk prediction can be carried out by utilizing a machine learning model, and the development of child prediabetic people into diabetic patients is effectively delayed or prevented.

The application relates to the technical field of medical diagnosis, in particular to a tongue diagnosis system for screening diabetes, which comprises the following steps: a tongue sensing module collects tongue physiological data, oral environment parameters and user basic health information; a dynamic tongue twin body construction module constructs a digital twin body, simulates tongue physiological state changes through a microcirculation evolution algorithm, and obtains simulation data; an intelligent analysis hub identifies diabetes-related tongue abnormal features and evaluates a risk level based on the simulation data and in combination with an improved Transform-CNN model; a visual interaction unit constructs an interactive virtual tongue image scene, real-time renders tongue morphology, metabolite distribution and microcirculation state, and generates a screening report and intervention suggestions; and an adaptive screening regulation unit predicts a diabetes trend, dynamically adjusts screening frequency and intervention schemes according to the diabetes risk level and the diabetes screening report. Thus, the problems of low precision and poor adaptability in the prior art are solved.

The invention discloses a diabetes risk prediction method based on high-dimensional disease embedding and health data fusion. Comprising the steps of data acquisition and preprocessing, construction of a high-dimensional disease embedding space, calculation of an individual health condition vector, deduction of a health score vector based on physical examination data, a hybrid prediction model for risk assessment, and model training and deployment. According to the method, a hybrid machine learning architecture is adopted, firstly, a health condition vector, a health score vector and original physical examination data are effectively integrated through a special fusion module, and then fused features are input into an integrated prediction model to generate a highly accurate diabetes risk score; according to the method, the prediction performance is remarkably improved, and the area (AUC) under the working characteristic curve of a subject reaches 0.84.