76 results about "Keywords diabetes" patented technology

The invention relates to the technical field of medical health big data analysis, in particular to a diabetic nephropathy risk identification method and system based on big data analysis, and the method comprises the following steps: screening key index features of risk patients through big data analysis based on health risk data of diabetic nephropathy patients; association of key indexes of the risk patients is analyzed, risk index interaction features are extracted, risk feature constraint conditions are determined, virtual sample parameters are identified through multi-dimensional data association, and a virtual sample set of the risk patients is established. According to the method, interaction characteristic values are extracted by analyzing key indexes of risk patients, high-risk index association is accurately captured, coverage and balance are enhanced based on multi-dimensional data association, non-stationary influence is solved by combining time sequence key change rate and shear point identification, and accurate matching is realized by analyzing offset rate and change trend. And the overall risk level is quantitatively evaluated by integrating interval data weighting, so that the risk identification comprehensiveness and the result reliability are improved.

The invention relates to the technical field of data analysis, in particular to a diabetes clinical test data intelligent clustering analysis system and method based on federal learning. Comprising a data acquisition and preprocessing unit; a federal privacy protection unit; the dynamic clustering modeling unit is used for constructing a clustering model of self-adaptive diabetes data features, and realizing joint clustering analysis of multi-source heterogeneous data by adopting a hierarchical federal architecture and a dynamic parameter aggregation algorithm and combining a diabetes course time decay factor and a clinical feature weight adjustment strategy; a double-track verification optimization unit; and an intelligent decision support unit. According to the method, the incidence matrix of the diabetes disease course time decay factor and the clinical characteristics is introduced, the dynamic weight vector is constructed and applied to clustering distance calculation, so that the model can adapt to dynamic changes of the clinical characteristics in different disease course stages, the adaptability to multi-center heterogeneous data is improved, and the stability of a clustering result is enhanced.

The invention discloses a heterogeneous double-flow fusion method and system for diabetic retinopathy grading. The method comprises the following steps: obtaining an output result of diabetic retinopathy grading by utilizing a heterogeneous double-flow architecture; processing an input fundus image into images with different resolutions; extracting global context features from the low-resolution image by using a lightweight visual Transform model distilled by composite knowledge, and extracting local focus features from the high-resolution image by using a convolutional neural network model; performing interactive fusion on the global context features and the local focus features of the double-branch architecture through a symmetric bidirectional cross attention fusion module to obtain enhanced fusion feature representation; and finally, inputting the fusion features into a classifier, and outputting a severity grading result of the lesion. The method aims at improving the accuracy and robustness of hierarchical diagnosis through deep analysis of global information and local details, and can be applied to the medical fields of clinical computer-aided diagnosis, eye image analysis and the like.

The invention relates to the technical field of medical data processing, in particular to a diabetes data sharing and analysis system based on a cloud platform, which comprises a diabetes data co-processing module, a patient feature hierarchical management module, a diabetes associated path analysis module and an intelligent semantic reasoning module. According to the invention, data of diabetic patients in medical institutions are collected through the cloud platform, data accuracy and effectiveness are improved, interference factors are eliminated, multi-source data are combined, redundancy and conflicts are reduced, cross-institution data integration capability is improved, patient features are processed in a layered manner through feature matching and priority ranking, a layered feature network structure is formed, and the accuracy and effectiveness of the data are improved. The node association strength and the path relation are analyzed, weak nodes are removed, interaction nodes are expanded, the path analysis depth and research value are improved, keywords and paths are matched based on semantic reasoning, high-frequency nodes and semantic paths are screened, the query accuracy and practicability are optimized, and the data analysis intelligent level and management efficiency are enhanced.

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 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 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 present invention relates to providing a personalized rehabilitation management method and system for diabetic patients based on a knowledge graph. By obtaining the changes in the patient's physical indicators, querying the diabetes rehabilitation knowledge graph, outputting a personalized rehabilitation plan containing quantitative execution indicators, and using the Proximal Policy Optimization reinforcement learning algorithm to monitor and evaluate the execution process of the rehabilitation plan. The method aims to improve the rehabilitation effect of diabetic patients, reduce the occurrence of complications, and improve the quality of life.

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 relates to a diabetic macular edema treatment effect prediction system, method and equipment, and belongs to the technical field of biomedicine. Comprising the steps of obtaining and preprocessing clinical data of a patient before treatment to obtain effective data; performing feature screening on the data to obtain feature data; constructing a prediction model, and training the prediction model by using the feature data to obtain an optimal vision recovery prediction model and an optimal edema regression prediction model; according to the two types of prediction models, a combined prediction model is constructed by adopting logic weighted fusion; inputting clinical data of a patient to be treated into the two types of prediction models to obtain a prediction result; and according to a prediction result, carrying out interpretability analysis on the optimal prediction model and the combined prediction model to obtain key characteristic variables, and further obtaining the probability of vision recovery and edema regression of the to-be-treated patient after treatment. According to the method, comprehensive and multi-dimensional sources of prediction variables are ensured, and the limitation of only depending on image data or subjective evaluation is effectively overcome, so that the prediction precision is improved.

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 invention discloses a disease early screening method based on a Stacking integrated model. Comprising the following steps: firstly, carrying out abnormal value processing by adopting a box graph theory, secondly, carrying out missing value processing by adopting a layered mean value filling method, carrying out correlation analysis and carrying out data cleaning to obtain a preprocessed data set, then, selecting several types of better base learners by applying an AUC index value, and finally, integrating the selected base learners by using a Stacking strategy, so as to obtain a data set; a new feature training set and a new test set are obtained through K-fold cross validation, and logistic regression is selected as a meta-learner for disease prediction. According to the method provided by the invention, the method is applied to experimental analysis of a pima Indiana diabetes data set, and the related evaluation indexes of the Stacking integrated model and the intermediate machine learning model are compared, so that the obtained Stacking integrated model has more excellent classification prediction performance, and can provide auxiliary decision information support for early disease screening.

Exemplary embodiments of the present disclosure are directed towards artificial intelligence-based system for automated preventative health screening and data-driven health risk analysis. The system integrates Generative AI with the CDC's Syndemic Model to enable anonymous, stigma-free health screening and care linkage. The system leverages oral health as neutral entry point to assess interconnected risks across sexual, mental, and behavioral health domains. Using Large Language Models trained on validated sources and AI-based image analysis of oral and skin abnormalities, system computes “Chance screening Scores” to classify risks for communicable (e.g., HIV, STIs, Mpox) and non-communicable diseases (e.g., diabetes, hypertension). Accessible through mobile apps / QR codes without requiring login, system generates Unique IDs for test kits, telehealth services, and rewards. Features include actionable recommendations, gamified Stigma Meter, geographic insights, language translation, and data control options. This innovative solution fosters informed decision-making, reduces stigma, and empowers users to manage their health confidentially and effectively.

The invention belongs to the technical field of diabetes diagnosis, and particularly relates to a diabetes prediction model construction method based on ensemble learning and a prediction system. The method comprises the following steps: step 1, carrying out preprocessing and feature screening on a data set; 2, dividing a training set and a test set; 3, searching and extracting positive samples in the training set, randomly generating positive samples obeying normal distribution according to the mean vector and the covariance matrix of the positive samples in the training set, adding the newly generated positive samples into the training set, and continuously repeating until the proportion of the negative samples to the positive samples is balanced; and 4, learning the model by using the processed training set machine. The invention also provides a diabetes prediction system constructed by using the machine learning model. The method can effectively improve the prediction performance of the prediction model through the balance of the sample data and the integrated learning of the single classifier, and has a good application prospect.

The invention discloses a mobile education vehicle for hospital diabetes knowledge spreading, the structure of the mobile education vehicle comprises a main body, a display screen, a bookshelf and an infrared induction module, the front end of the main body is provided with the display screen, the bookshelf is installed on the top end face of the main body, and the infrared induction module is located at the bottom of the front end of the main body; the main body comprises transmission wheels, a storage groove, a display device, a voice recognition module and a loudspeaker module, the main body is supported by the transmission wheels to move, knowledge of diabetes can be propagandized in the moving process of the main body, the infrared induction module can recognize whether an obstacle exists at the front end of the main body or not, obstacle avoidance can be conducted in the moving process of the main body, and the practicability is high. When the main body moves and publicizes, the device can automatically stop when encountering a patient, and the patient can perform inquiry and learning through the cooperation of the voice recognition module and the loudspeaker module.

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

This invention relates to a grading method for diabetic retinopathy based on a local-global interactive dual-branch network, belonging to the fields of deep learning, medical image analysis, and computer-aided diagnosis. It includes: inputting the generated initial feature sequence into a global scanning module; using dilated reparameterized convolution to capture multi-scale structural prior information; inputting the feature map with structural prior information into a core dual-branch LoGo module; the local flow extracting fine-grained lesion features through depthwise separable convolution; the global flow generating spatially variable convolution kernels based on the global context through contextual hybrid dynamic convolution; and performing bidirectional cross-modulation through an adaptive multi-scale feature interaction aggregation module to sharpen local details for global semantics and filter local noise from the global context, ultimately outputting a disease severity grade. This invention explicitly simulates the cognitive strategies of clinicians, significantly improving grading accuracy while maintaining computational efficiency.

A kind of processing method and system of fundus image for diabetes prediction, comprising the following steps: step S100, the original fundus vascular image is preprocessed;Step S200, identify the vascular information in fundus vascular image, and convert to the coordinate and morphology data of fundus vascular and store;It specifically includes: step S210, extract the arteriovenous center line of fundus;Step S220, identify the key point of fundus vascular;Step S230, the identified fundus vascular breakpoint is connected;In the two vascular breakpoints belonging to the same fundus vascular, it is connected using right-angle broken line mode.Step S240, the fundus vascular center line coordinate, fundus vascular bifurcation relationship is stored;Step S300, according to the fundus vascular coordinate and morphology data, extract the fundus vascular feature.This method can also include: step S400, the fundus vascular feature extracted in step S300 is handled by meta-classifier model, to carry out early screening or prediction of diabetes, can greatly improve the prediction accuracy.

The application discloses a kind of based on multi-source medical text construction traditional Chinese medicine diabetic nephropathy knowledge graph method and system, including following module: data integration module: integration diagnosis and treatment guideline and electronic medical record, after redundancy, terminology standardization constructs unified medical data set;Mixed labeling and entity recognition module: fusion artificial labeling and BERT-BiLSTM-CRF model automatically identifies traditional Chinese medical syndrome, traditional Chinese medicine and other complex entities;Relationship modeling module is based on semantic embedding and multi-label classification algorithm, accurately constructs traditional Chinese and western medicine association rules;Atlas application module: entity relationship is converted into attribute graph structure, supports graph database interactive query, realizes diagnosis and treatment recommendation, contraindication early warning and curative effect analysis.Through the above scheme, the present application significantly improves the accuracy of differentiation, reduces the risk of drug contraindication, provides efficient, interpretable decision support for the prevention and treatment of diabetic nephropathy, has important clinical value and popularization potential.

The application discloses a type 2 diabetes heart failure risk prediction system and method, and belongs to the technical field of medical diagnosis and risk assessment. The prediction system comprises the following modules: a data and feature engineering module, which is responsible for standardization processing of data and screening of key prediction factors, and obtains a core feature set for machine learning; a model construction and selection module, which uses the core feature set and trains multiple machine learning algorithms in parallel; through cross-validation and comprehensive performance evaluation, the best model is selected as a prediction model; and a model deployment and application module, which converts output results of the prediction model into a clinically usable static nomogram or online tool, and performs visual output. The application predicts by integrating clinical variables and adopting a machine learning algorithm, and provides a static nomogram and a dynamic Web application, realizes heart failure risk assessment without relying on NT-proBNP detection, and can improve the prevention and management efficiency of cardiovascular diseases.

Disclosed herein is a healthcare management system and method utilizing machine learning based predictive analytics to improve patient adherence rates for chronic care management (e.g., diabetes). An example system may include a computing device configured to establish a customer analytical record to synthetize over a plurality of selected attributes to form a patient centric view of patient behaviors, attitude, characteristics based upon data related to chronic disease state management and social determinants of health, consumer experience to determine, using a predictive artificial intelligence model, a patient's predictive adherence for next 1-3 months, and determine tailored intervention plans to engage patients for continuing adherence.

This invention belongs to the field of diagnostic technology for diabetic complications, and relates to a diagnostic method for type 2 diabetes complications based on multi-source heterogeneous feature transfer. The method includes: extracting features from CGM sequence information, clinical indicators, and past medication information, and dividing them into training and testing sets; using a Gaussian mixture clustering algorithm on the training set to obtain K Gaussian models and the membership class of each sample based on a preset number of clusters K; pre-training samples from each class to obtain K diagnostic models; for each diagnostic model, weighting samples from other classes based on the membership matrix and then transferring the weighted samples from other classes for training to obtain K final models; and using the final models to diagnose complications of type 2 diabetes. Its beneficial effect is that it integrates multi-source information in information processing, solving the problem of fragmentation between sub-models of different subgroups, enabling the models to be fully trained, and improving the accuracy of the models in diagnosing complications.

The application relates to a non-invasive diabetic nephropathy grading and prediction method supporting convenient screening, which comprises the following steps: collecting color fundus images and non-invasive clinical characteristics of diabetic patients, pre-processing to obtain a multi-modal data set, and setting annotations of multiple severity degrees of diabetic nephropathy; constructing a deep learning model A1 to obtain a diabetic nephropathy severity prediction probability distribution based on the color fundus images according to the multi-modal data set; constructing a deep learning model A2 to obtain a diabetic nephropathy severity prediction probability distribution based on clinical data according to the multi-modal data set; constructing a fusion model, the fusion model fuses the obtained diabetic nephropathy prediction probabilities through an adaptive weight distribution algorithm, dynamically adjusts the weights according to the prediction results output by each model, calculates a comprehensive diabetic nephropathy severity score, and outputs a final diabetic nephropathy prediction grading result according to the score.

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

Disclosed herein is a healthcare management system and method utilizing machine learning based predictive analytics to improve patient adherence rates for chronic care management (e.g., diabetes). An example system may include a computing device configured to establish a customer analytical record to synthetize over a plurality of selected attributes to form a patient centric view of patient behaviors, attitude, characteristics based upon data related to chronic disease state management and social determinants of health, consumer experience to determine, using a predictive artificial intelligence model, a patient's predictive adherence for next 1-3 months, and determine tailored intervention plans to engage patients for continuing adherence.

The invention relates to a diagnosis and treatment data management and collaborative diagnosis and treatment method for diabetic retinopathy, which is applied to a medical care terminal, and comprises the following steps: in response to successful login of a user, displaying a homepage of the medical care terminal; skipping to a medical record auditing page under the condition that the user identity is a medical worker and a request for viewing the medical record auditing page is received; the medical record auditing page is used for displaying the medical record data with the marked auditing state; the medical record data is uploaded by a patient end, detection equipment and / or a hospital information system; when a selection operation on the target medical record data whose auditing state is to be audited is received, displaying the target medical record data in response to a preview operation triggered through a preview component; and when a selection operation on the auditing passing component is received, updating the auditing state of the target medical record data to be auditing passing. Therefore, by means of unified state marking and medical record data summarization, medical record state chaos caused by operation habit differences is reduced, and the problems that medical data are scattered and difficult to integrate are solved.

The invention relates to a data fingerprint method and system for public health data synchronization, and the method comprises the following steps: 1, building a public health service scene data quality control model such as a health record service, a hypertension service and a diabetes service, 2, collecting the existing public health service data, carrying out the ANSIC coding of the data, and storing the data in a database; the method comprises the following steps: 1, carrying out weight statistics on existing basic data, 3, carrying out secondary step-counting classification on the counted data weight, and carrying out recoding arrangement on the data by adopting a binary tree classification method, 4, generating a binary tree number new coding table C1, C2, C3... Cn, and carrying out continuous sorting optimization on the binary tree coding table according to a subsequent data input result, so that the operation efficiency is improved, and the calculation cost is reduced. And 5, generating a public health data field coding file, and carrying out secondary coding to form a smaller field.

Systems and methods are described that provide a dynamic reporting functionality that can identify important information and dynamically present a report about the important information that highlights important findings to the user. The described systems and methods are generally described in the field of diabetes management, but are applicable to other medical reports as well. In one implementation, the dynamic reports are based on available data and devices. For example, useless sections of the report, such as those with no populated data, may be removed, minimized in importance, assigned a lower priority, or the like.