37 results about "Diabetes risk" patented technology

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 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 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 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 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.

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.

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.

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.

The application relates to a diabetes risk detection method and device, computer equipment and a medium. The method comprises the following steps: S1, determining abnormal data in real-time detection data of a to-be-detected object; the abnormal data comprises an abnormal index and a corresponding index value; S2, determining a core index in each abnormal index based on a correlation coefficient between each abnormal index and diabetes; S3, screening a candidate factor matching a normalized core index from a knowledge graph; the knowledge graph comprises a plurality of factors, and each factor comprises an index and an index value; S4, calculating a similarity based on the index value of the candidate factor and the index value of the core index, and determining a target factor with a similarity greater than a similarity threshold value; and S5, determining an associated factor associated with the target factor in the knowledge graph, and determining a comprehensive risk value of the to-be-detected object based on the risk values of the target factor and the associated factor. The method can improve accuracy.

The invention discloses a deep learning personalized nutrition recommendation method and system based on a user portrait, and the method comprises the steps: collecting user physiological indexes, diet behaviors and environment parameters through a wearable device, intelligent tableware and an environment sensor, and obtaining collection data; inputting the collected data into a dynamic causal time sequence model, calculating the time-varying causal intensity of a diet event and a physiological index by adopting a convergent cross-mapping algorithm, and dynamically adjusting the length of a sliding window according to the maximum physiological response delay, a diabetes risk factor and a glycosylated hemoglobin deviation value; and extracting a user portrait vector containing metabolic phase shift through a bidirectional gating network. According to the method, a dynamic nutrition recommendation system of metabolism perception is constructed, and the core problems of dynamic metabolism response modeling and abnormal state real-time regulation and control are solved through a multi-modal data fusion and pathology-driven adaptive learning mechanism.

The invention discloses a multi-mode diabetes risk prediction method combining traditional Chinese medicine and western medicine, and relates to the problems that in the diabetes risk prediction technology, traditional Chinese medicine diagnosis information is difficult to effectively utilize, and long-term risk prediction accuracy is insufficient. Diabetes is a common metabolic disease, can cause serious complications such as retinopathy, cardiovascular and cerebrovascular diseases and the like after long-term development, and causes huge burden to health of patients and public health of society. Clinical research shows that early risk assessment and intervention are carried out on prediabetic patients, so that the probability that the prediabetic patients develop into diabetes and complications can be remarkably reduced. However, the existing prediction method mainly depends on clinical indexes and statistical models, lacks systematic utilization of tongue condition, pulse condition and other traditional Chinese medicine diagnosis information, and is limited in accuracy in the aspect of long-term risk prediction. In order to solve the problem, the invention provides a multi-mode diabetes risk prediction method combining traditional Chinese medicine and western medicine. Experiments show that the method has the following advantages: (1) the characterization capability of the model on the tongue image, the pulse image and the diagnosis text is enhanced through multi-modal contrast learning, and the utilization value of traditional Chinese medicine diagnosis information is improved; and (2) multi-source features such as the tongue image, the pulse image, the retina image and clinical indexes are fused, and the prediction performance on the 10-year risk of diabetes is remarkably improved. The method can be applied to risk assessment of prediabetic people.

The invention relates to a multi-modal data-driven diabetes risk prediction and personalized intervention method, which is characterized in that a prediction result is transmitted to a user side and a medical staff side through a cloud platform prediction result processing module, a primary intervention scheme is directly generated at the user side, and meanwhile, a medical staff gives a comprehensive intervention scheme and transmits the comprehensive intervention scheme to the user side; according to the method, a comprehensive personalized intervention scheme is implemented, integration operation is carried out on multi-source heterogeneous data by adopting fusion operation, the fusion algorithm can adopt multiple modes to carry out integration operation on screened data with identification features, a final accurate risk prediction result is generated, the integrity and accuracy of prediction data are ensured, and the risk prediction efficiency is improved. Defects caused by data deviation, abnormity and the like are avoided, and the prediction capability is improved.

The invention relates to a kit and a system for diagnosing diabetes. The kit includes several cells to contain several concentrations of an HDAR2 receptor activator or an HDAR3 receptor activator or an ester aqueous solution thereof. The kit provided by the invention can be used for effectively diagnosing diabetes mellitus by evaluating the sensitivity degree of a subject to the HDAR2 receptor activator or the HDAR3 receptor activator or the ester thereof. The system disclosed by the invention can be used for quickly obtaining a detection result through several simple steps of operation in a short time, realizes multi-scene real-time detection in wards, physical examination centers, homes and the like, and is an objective, quick and effective biological detection means for diabetes risk early warning.

The system of the embodiment aims to personalize and optimize diabetes risk assessment and management. [Solution] A system according to an embodiment includes a collection unit, a prediction unit, a provision unit, a monitoring unit, and a platform unit. The collection unit collects health checkup data. The prediction unit analyzes the data collected by the collection unit to predict a diabetes risk score. The provision unit provides individual risk assessment and advice based on the risk score predicted by the prediction unit. The monitoring unit collects and analyzes data using monitoring devices. The platform unit provides a patient community platform.

The invention relates to a diabetes risk detection method and device, computer equipment and a medium. The method comprises the following steps: S1, determining abnormal data in real-time detection data of a to-be-detected object; the abnormal data comprises abnormal indexes and corresponding index values; s2, determining a core index in each abnormal index based on a correlation coefficient between each abnormal index and diabetes; s3, candidate factors matched with the normalized core indexes are screened out from the knowledge graph; the knowledge graph comprises a plurality of factors, and the factors comprise indexes and index values; s4, calculating similarity based on the index values of the candidate factors and the index values of the core indexes, and determining the candidate factors of which the similarity is greater than a similarity threshold as target factors; and S5, determining an association factor associated with the target factor in the knowledge graph, and determining a comprehensive risk value of the to-be-tested object based on the risk values of the target factor and the association factor. By adopting the method, the accuracy can be improved.

The invention provides a diabetes detection method based on urine routine physical examination data, and relates to the technical field of data processing. The method comprises the following steps: acquiring the urine routine physical examination data of a patient; performing double filtering on the urine routine physical examination data to obtain a target smooth sequence; inputting the target smooth sequence into a context sensing data fusion model, and outputting a feature set; performing principal component extraction on the feature set through a principal component analysis algorithm to obtain a low-dimensional feature set; performing enhanced recursive feature elimination on the low-dimensional feature set to obtain an optimal feature subset; constructing a diabetes detection model; and inputting the optimal feature subset into the diabetes detection model, and outputting a diabetes risk detection result.

The invention relates to the technical field of diabetes risk assessment, and discloses a diabetes risk assessment method and system, and the method comprises the steps: collecting user data, obtaining a diabetes diagnosis standard, and carrying out the judgment of a crowd according to the user data and the diabetes diagnosis standard; establishing a life correction model, a saccharification deviation model and a glycosylation accumulation model, and processing the user data to obtain analysis data; when the crowd is judged to be a non-diabetic crowd, establishing a disease assessment model, calculating a basic risk score by the disease assessment model based on a life correction model, auditing and correcting the basic risk score through a disease condition to obtain a glycosylation correction risk score, and mapping the glycosylation correction risk score into a disease risk level; and when the crowd is determined to be the diabetic crowd, establishing a control evaluation model, calculating a basic control risk level by the control evaluation model based on the life correction model and the glycosylation accumulation model, and auditing and correcting the basic control risk level through a control condition to obtain the control risk level.