121 results about "Chronic disease" patented technology

The application discloses a kind of based on deep learning's slow disease individualized development path planning method, it is related to intelligent health management technical field, including: the construction standardization health status dataset, and generate multidimensional feature set by representation learning process;With multidimensional feature set as the input of association analysis, extract the feature set representing different health goals, assign target weight to generate individual multi-objective health feature table;Individual multi-objective health feature table is input into deep reinforcement learning model, through the adaptive learning of multilevel sub-strategy, corresponding strategy weight is assigned to different health goals, and candidate intervention path is generated;In candidate intervention path training, risk and benefit feedback are combined, and dynamic balance intervention strategy is output.The application realizes the intervention contribution degree coordination and path weight adaptive convergence effect on the whole path scale to multiple health goals by using dynamic balance algorithm in candidate intervention path training process.

A device may receive physical activity data for a subject, wherein the physical activity data comprises raw acceleration data. A device may derive a plurality of metrics from the raw acceleration data, wherein the plurality of metrics comprise a plurality of pattern-related metrics. A device may input the plurality of metrics into a deployed machine learning model. A device may be configured to perform receiving, from the deployed machine learning model, a physical activity score. A device may calculate a risk score for at least one disease or condition based on the physical activity score.

The present invention relates to a composition for the improvement and treatment of muscle diseases and cachexia, wherein diflunisal has the effect of promoting the differentiation and proliferation of myogenic cells, and further has the effect of improving and treating cachexia induced by chronic diseases such as muscle diseases or cancer through effects such as increased muscle mass and recovery of muscle strength.

The application discloses a prognosis risk prediction model construction system and method based on chronic disease real world data, comprising a multi-source real world data acquisition and access module, a data standardization and quality control module, a multi-dimensional feature engineering and screening module, a comorbidity feature fusion and data set construction module, a prognosis risk prediction model training and optimization module, a model multi-dimensional verification and evaluation module, a model deployment and risk stratification output module; a special multi-source real world data acquisition and quality control system for chronic liver disease, diabetes and hypertension is constructed, data standardization processing is realized in combination with a chronic disease clinical diagnosis and treatment guideline, through multi-dimensional missing value, abnormal value processing and data deduplication integration, the quality and availability of the real world data are effectively improved, the core problem of disorderly and low-quality real world data in the prior art is solved, and a high-quality data source basis is provided for model construction.

The present invention relates to a system and method for recommending health functional foods to elderly patients with geriatric chronic diseases. According to the present invention, a method for recommending health functional foods, performed by a system for recommending health functional foods, comprises the steps of: receiving, from a target patient with a geriatric chronic disease, response data obtained from responses to a medical inquiry form or questionnaire; performing natural language processing on the response data; inputting the natural-language-processed response data into a first learning model to classify a mental disorder and a severity level of the target patient; receiving personal information and EMR data of the target patient; and inputting the personal information, EMR data , and information on the mental disorder and severity level of the target patient into a second learning model to recommend one or more health functional foods to the target patient.

This application provides a method, apparatus, and computer device for joint risk assessment of chronic diseases and comorbidities, relating to the field of medical and health technology. The method includes: performing hierarchical preprocessing on health examination-related data from multiple subjects to generate a standardized dataset containing single-disease labels and comorbidity combination labels; inputting the target data into a single-framework multi-objective collaborative ensemble model to obtain single-disease and comorbidity prediction values; the single-disease and comorbidity prediction values ​​are obtained synchronously through a single inference by the single-framework multi-objective collaborative ensemble model; calculating a comorbidity risk gain value based on the single-disease and comorbidity prediction values, and generating a structured assessment result based on the comorbidity risk gain value. This method can improve the accuracy of joint risk assessment of chronic diseases and comorbidities.

This invention relates to the intersection of traditional Chinese medicine modernization and intelligent healthcare, specifically falling under the category of pre-chronic disease screening and health management technology. More particularly, it relates to a method for diagnosing and treating pre-type 2 diabetes based on traditional Chinese medicine state theory, along with electronic devices and computer-readable storage media, constructing a complete, human-machine collaborative closed-loop diagnostic and treatment process. This method achieves objective data collection through portable devices using the four diagnostic methods of traditional Chinese medicine, utilizes a multi-algorithm fusion model to accurately identify the "potential pathological state" of pre-T2DM, and intelligently generates personalized intervention prescriptions based on this. Finally, it quantifies the effects and dynamically optimizes the treatment plan through data re-collection and model re-evaluation. This solves the problems of strong subjectivity in traditional Chinese medicine diagnosis and treatment, disconnection from modern technology, and lack of quantitative feedback and continuous adjustment after intervention. It achieves full-cycle, intelligent, and personalized health management for pre-T2DM, from identification and intervention to follow-up management.

The present invention is directed to the use of antiviral agents, in particular valomaciclovir stearate and its polymorph Form A, as well as H2G (omaciclovir) to treat multiple sclerosis in combination with other agents, as well as the use of these agents to treat diseases and conditions such as chronic mononucleosis, long COVID, chronic fatigue syndrome, fibromyalgia, Crohn's disease, ulcerative colitis, rheumatoid arthritis, systemic lupus erythematosus, Graves' disease, Alzheimer's disease, mesial temporal lobe seizures, Epstein-Barr-virus-linked autism, or an Epstein-Barr-virus-linked cancer.

The application discloses a traditional Chinese medicine body milk and a preparation method thereof. The traditional Chinese medicine body milk comprises the following components by weight: 5-15 parts of traditional Chinese medicine active ingredients derived from an ancient prescription of Danggui Yinzi, 1-3 parts of vitamin E, 0.5-2 parts of ceramide NP with a purity of greater than or equal to 95%, and 0.1-0.5 parts of natural borneol with a purity of greater than or equal to 99%, and the rest is an oil phase matrix, an aqueous phase matrix and an emulsifier. The traditional Chinese medicine active ingredients comprise angelica extract, astragalus polysaccharide, sophoramine and glycyrrhizic flavone, and are obtained by a low-temperature warm extract process at 40-45 DEG C. The preparation method comprises the following steps: raw material pretreatment, warm extract extraction, filtration and purification, precise compounding and fusion, emulsification and molding, and cooling and filling. The product has the advantages that skin irritation is less than 0.3, 24-hour moisturizing power is greater than or equal to 78%, the retention rate of herbal active ingredients is greater than or equal to 83.2%, and absorption is achieved within 5 minutes after smearing, and is specially used for improving the problems of dry skin, itching and difficult healing of wounds caused by blood deficiency and dryness caused by old age and chronic diseases.

This invention proposes a health monitoring method and device based on a smart bracelet. The method uses the smart bracelet as the executing entity and includes the following steps: S1: Acquiring a multimodal physiological signal set and an environmental interference data set of the monitored object. The multimodal physiological signal set includes electrocardiogram (ECG) signals, heart rate signals, blood pressure signals, blood oxygen saturation signals, skin conductance signals, and body temperature signals. The environmental interference data set includes motion state data, light intensity data, wearing contact pressure data, and ambient temperature data. S2: Preprocessing the multimodal physiological signal set and the environmental interference data set. This invention can achieve accurate, efficient, and personalized health monitoring, output health level and abnormality tracing information, and trigger early warnings when necessary. It is suitable for daily health management, exercise monitoring, and disease early warning scenarios for different groups such as children, adults, and patients with chronic diseases.

This invention relates to the field of health and medical data processing technology, and particularly to a method for chronic disease risk management based on community grids. The method includes constructing an initial spatial environmental potential field based on point-of-interest weights; calculating the risk exposure integral sequence and instantaneous potential gradient by combining user trajectories; if the gradient exceeds a threshold, adjusting parameters and using a matrix contour algorithm to extract stress-related abnormal motifs from physiological monitoring data; reconstructing the potential field by statistically analyzing the frequency of group motif attacks; finally, calculating the cross-correlation between the exposure integral and the attack frequency sequence to obtain a causal index; and combining this with health records to output a chronic disease risk rating and intervention instructions. In this invention, by introducing an instantaneous potential gradient to feedforward and regulate the extraction parameters of physiological motifs, the technical problem of traditional fixed-parameter sampling easily missing environmental stress physiological responses is solved, enabling the underlying algorithm to accurately capture users' stress-related abnormal physiological motifs during sudden environmental changes.

This invention relates to a Five Elements Pill composed of five traditional Chinese medicines: Astragalus membranaceus, Polygonatum sibiricum, Rehmannia glutinosa, Poria cocos, and Citrus reticulata peel, and its preparation method. In this Five Elements Pill, Astragalus membranaceus is the principal ingredient, Polygonatum sibiricum and Rehmannia glutinosa are the assistant ingredients, Poria cocos is the adjuvant ingredient, and Citrus reticulata peel is the guiding ingredient. This invention, based on the principles of the Five Elements theory and the philosophy of syndrome differentiation and treatment, carefully selects and combines these five traditional Chinese medicines. The formula is precise and rational, with significant therapeutic effects, suitable for medicinal and health maintenance use by middle-aged and elderly people, and can meet their needs for disease prevention, chronic diseases, and geriatric diseases. This invention combines modern and traditional methods, making it practically applicable and producing high-quality, qualified products that meet social needs.

The application discloses a medication compliance monitoring and reminding platform for chronic disease patients in a large internal medicine department, and belongs to the technical field of medical intelligent monitoring. The platform comprises 10 modules in cooperation and linkage, adopts a distributed architecture for deployment, collects full-amount data related to patient medication through a multi-source data acquisition module, and transmits the data to a core operation and analysis module after processing by a special noise reduction formula. The core operation and analysis module realizes precise compliance evaluation, medication trend prediction and reminding strategy optimization based on multiple special mathematical formulas. An intelligent reminding module provides individualized and hierarchical reminding. A medical staff end management module realizes patient whole-process management and intervention scheme formulation. A data storage and encryption module ensures data security. A compliance intervention module realizes precise intervention and effect tracking. An abnormality early warning module realizes real-time early warning and linkage disposal. A cross-terminal synchronization module and a system self-checking and calibration module ensure stable operation of the system and data synchronization.

This invention belongs to the field of biomedicine, specifically relating to siRNA that inhibits AR gene expression, comprising a sense strand and an antisense strand, wherein the sense strand and the antisense strand are at least partially anticomplementary to form a double-stranded region, and the sense strand or antisense strand consists of 19-25 nucleotides with blunt ends of complementary nucleotide sequences; or the antisense strand consists of 27 nucleotide sequences with overhangs. The siRNA of this invention can maintain long-term efficacy with a single dose, exhibiting good compliance in patients with chronic diseases such as androgenetic alopecia. Furthermore, it has lower off-target toxicity and fewer side effects compared to small molecule chemical drugs and hormone drugs.

Described herein are treatment regimens, therapies, pharmaceutical formulations and methods of using quercetin to prevent, treat, and / or protect from muscle wasting including 1) sarcopenia associated with aging; and 2) cachexia associated with chronic diseases and their treatments including but not limited to cancer, cardiovascular disease, chronic obstructive pulmonary disease (COPD), HIV, and kidney disease.

This invention discloses an online health management method and system based on a smart chip, relating to the field of data processing technology. The method includes: online collection of user characteristics of a target user, and acquisition of a health processing plan provided by a first person and a management processing plan provided by a second person; inputting the user characteristics into a management demand analysis agent configured within the smart chip, and outputting a predicted management demand information distribution; calculating the management matching degree distribution between the management processing plan and the predicted management demand information distribution, and processing and acquiring the online degree based on the target user's online records; adjusting and optimizing the management processing plan based on the online degree and management matching degree distribution to obtain the optimal management processing plan, and then pushing online health management information. This invention effectively improves the accuracy of non-medical aspects of online health management for chronic 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.

The present application relates to the technical field of medical health management, in particular to a chronic disease patient health management system based on big data, comprising: constructing a full-dimension personal health record containing static and dynamic information. The data analysis module performs abnormal pattern mining on continuous sign data, and generates a trend graph by associating periodic test data with historical records. The data correlation module constructs a risk correlation matrix based on environmental exposure data, which quantifies the contribution of environmental behavior factors to health indicators. The risk modeling module integrates the above information. The prediction and intervention generation module uses a chronic disease progression prediction model to calculate and synchronously output individualized disease risk warning signals and the corresponding phased health intervention programs. The present application realizes the quantitative attribution of health risk factors and dynamic, phased precision health management.

This utility model relates to the field of smart pillbox technology, and more particularly to a control system for a smart pillbox. By integrating a control module, a dispensing module, an interaction module, a reset module, and a reminder module into a collaborative architecture, it solves the technical problem of traditional pillboxes having limited functionality. The control module uniformly schedules the motor drive chip and servo motor of the dispensing module to achieve precise rotation of the medicine container and timed dispensing. The mechanical buttons of the interaction module ensure the reliability of parameter settings and action control. The photoelectric sensor of the reset module detects the reset status of components through a current-limiting resistor, preventing mechanical misalignment. The LED strip of the reminder module is controlled by a transistor drive circuit to provide visual reminders of medication time. The orderly collaboration of multiple modules under the management of the control chip significantly improves the automation level and operational accuracy of the pillbox, making it particularly suitable for the medication management needs of elderly users and patients with chronic diseases. This also solves the technical problem of the relatively limited functional design of current pillboxes.

This invention discloses a method and system for predicting chronic disease risk based on multimodal data fusion, relating to the field of chronic disease prediction. It includes: an upload module for uploading patient TCM syndrome description data and Western medicine physiochemical test data, classifying and storing them according to data type, and simultaneously establishing multi-source data index identifiers; and a processing module for performing semantic standardization processing on the classified TCM syndrome data and outlier filtering on the Western medicine physiochemical indicator data to output normalized multimodal data. This invention integrates TCM syndrome and Western medicine physiochemical test data, standardizes semantic expressions, accurately filters out abnormal data, fully explores the correlation features and time-series change patterns between the two, dynamically allocates feature weights to achieve deep fusion, and constructs a TCM-Western medicine collaborative prediction logic, making the quantification of chronic disease risk levels more reasonable.

This invention discloses a method for medication risk warning and dynamic intervention using a smart pillbox for elderly patients with chronic diseases, relating to the fields of intelligent medical devices and chronic disease management technology. The method first constructs four types of chronic disease knowledge graphs: medication behavior, physiological indicators, drug interactions, and environmental conditions. Then, it processes the graphs using a GNN module with a GRU-like gating mechanism, weightedly fusing multi-time-step features using a time-step attention mechanism, and integrating similar patient features based on Jaccard similarity to generate comprehensive risk features. During the training phase, the risk level and type are output after feature optimization using a collaborative attention mechanism. In the inference phase, a similarity propagation mechanism is used to address the cold-start problem for newly enrolled patients, matching a three-level intervention rule base to generate personalized strategies, and iteratively optimizing the model based on intervention feedback. This invention achieves proactive and precise warning and dynamic intervention, improving risk assessment performance and patient medication adherence in different scenarios.

According to an embodiment of the present invention, an integrated care system for chronic disease management may comprise: a communication module for acquiring user information including health information from an electronic device of a user and institution information including local event information from a local institution; and a processor for providing customized information for improving the health of the user by using the health information.

This invention discloses a risk management system and method for chronic disease comorbidity in the elderly based on interpretable machine learning. It includes: a data acquisition module for acquiring health-related information of the target user; a feature construction module for processing and fusing the health-related information into a structured multidimensional feature vector; a risk assessment module internally deploying a pre-trained machine learning model to output a chronic disease comorbidity risk score for the target user based on the multidimensional feature vector; an interpretability analysis module analyzing the output of the machine learning model to generate interpretable results representing the key driving factors influencing the risk assessment results and their contribution; and a personalized recommendation module, based on the risk score and key driving factors, querying a pre-set rule knowledge base to generate and output a set of personalized health management recommendations for the target user. This invention integrates scale data, clinical indicators, and comorbidity structure into a model to form an interpretable risk score, meeting the needs for comprehensiveness, accuracy, and feasibility in chronic disease comorbidity management in the elderly.

The application belongs to the technical field of the Internet of Things and old-age health care, and particularly relates to a cognitive impairment chronic disease health management system and method based on the Internet of Things. The method is applied to a smart old-age care scene and comprises the following steps: a data acquisition step, in which an active acquisition and passive acquisition combination is adopted; a data transmission step, in which a 5G, WiFi and LoRa multi-network fusion transmission mode is adopted; an intelligent analysis step, in which a weighted fusion algorithm is adopted to fuse multi-source data and identify cognitive function abnormality features; a grading evaluation step, in which a four-dimensional weighted scoring model is used to calculate grading evaluation; and a grading intervention step, in which corresponding intervention measures are executed according to the grading evaluation result. The application realizes full-process automation of cognitive impairment chronic disease management, improves early screening coverage, delays disease progression, reduces care burden and improves the quality of life of the elderly.

The invention discloses a chronic disease whole-process digital medical treatment cloud service and medical insurance real-time supervision unified large model. The model comprises a technical architecture and a combination engine; the technical architecture comprises an infrastructure layer, a data base layer, a core technology layer, a business application layer and an ecological collaboration layer. The combination engine comprises federated learning and block chain combination, digital twinning and AI and big data combination, edge calculation and 5G and Internet of Things combination, NLP and knowledge graph and AR combination, quantum encryption and zero knowledge proof combination, and Web3.0 and DAO combination. Through cross-layer cooperation of a five-layer technical architecture and six technology combination engines, balance between data sharing and privacy protection is realized, cross-mechanism data islands are broken, data security compliance is guaranteed through technologies such as federal learning block chain quantum encryption, and the contradiction between data intercommunication and privacy protection in the prior art is solved; according to the digital twinborn AI big data combination, a full-process risk control system is constructed, and risk pre-prediction in-event intervention and post-event optimization are realized.

This invention relates to the field of wearable medical device technology, specifically to a flexible wristband for health monitoring of the elderly integrating multiple sensors. It includes a smartwatch body with four symmetrically arranged grooves on the bottom. Each groove has a first electromagnetic plate installed on its inner top wall, and a column is fixedly connected to the bottom of the first electromagnetic plate. A sleeve is fitted onto the outer wall of the column. The integrated blood glucose monitoring microneedle module of this invention uses a micron-level painless needle that can painlessly penetrate the stratum corneum to obtain interstitial fluid. Combined with multimodal analysis technology, the measurement accuracy reaches medical grade, solving the problem of insufficient accuracy in traditional non-invasive devices while avoiding the discomfort of invasive blood collection. This results in high compliance among the elderly. It integrates multiple monitoring functions such as blood glucose, blood pressure, heart rate, blood oxygen, arterial stiffness, and fall detection, achieving full coverage of chronic disease management and health risk warning. It eliminates the need to wear multiple devices, meeting the needs of the elderly for joint prevention and control of multiple diseases.

The application discloses a chronic disease prediction system based on machine learning, comprising a data acquisition module, a feature extraction module, a chronic disease prediction module and a hospitalization prediction module; the data acquisition module is used for collecting chronic disease related data and environmental parameters of each chronic disease prediction area, obtaining a per capita chronic disease distribution map in a first time period, preprocessing the per capita chronic disease distribution map, and generating a first chronic disease prediction image; the feature extraction module is used for obtaining a first chronic disease element and a second chronic disease element, and obtaining corresponding moving features and spatial features; the chronic disease prediction module is used for constructing a chronic disease element moving virtual track according to the moving features, and constructing a chronic disease prediction model for predicting the evolution of chronic diseases based on the chronic disease element moving virtual track; and the hospitalization prediction module is used for predicting hospitalization data corresponding to the evolution of chronic diseases, and providing a scientific basis for chronic disease early warning and planning.

This invention discloses an adaptive early warning and intervention system for chronic obstructive pulmonary disease (COPD), relating to the fields of telemedicine and intelligent chronic disease management. The system includes: collecting multi-source physiological and clinical data from COPD patients to obtain a multi-dimensional input dataset; extracting risk feature vectors; constructing and dynamically updating individual health profiles; inputting the risk feature vectors and clinical baseline data into an LSTM dynamic risk prediction model to output risk probabilities; performing adaptive early warning judgments based on risk probabilities and individual health profiles to output early warning levels; dynamically assembling and pushing personalized intervention plans from a structured intervention knowledge base; and continuously updating the adaptive rule base and prediction model parameters by collecting feedback data. This invention solves the technical problems of existing COPD remote management systems, such as frequent false alarms and missed alarms due to static judgment using fixed thresholds, insufficient targeting of early warning and intervention content, long response chains, and a lack of adaptive adjustment capabilities, thereby reducing the occurrence of false alarms and missed alarms.

The application discloses a kind of cross-modal chronic disease prediction methods based on artificial intelligence, belong to medical artificial intelligence field.The method first acquires four kinds of multi-source heterogeneous information of medical image, biochemical index, behavior data and text data;Second, construct the multi-modal encoder consisting of 3D-CNN, LSTM and BERT, respectively extract spatial, time sequence and semantic features;Subsequently, the cross-modal attention mechanism is used to fuse each modal feature, and the nonlinear law of chronic disease progression is captured by LSTM+attention network;Further introduce causal inference module, distinguish the causal effect and correlation of risk factors;Finally, according to the characteristics of patient population dynamically adjust model weight, realize the personalized risk prediction and progress curve estimation of chronic disease.The application solves the problem of ignoring data multi-modal, time sequence and causal confusion in the prior art, significantly improves the accuracy, explainability and model generalization ability of chronic disease prediction.

The application discloses a personalized health behavior incentive system and method based on blood pressure feedback, multi-dimensional data is collected through a PPG+ECG dual-mode sensor, an XGBoost model is combined to establish a behavior-blood pressure correlation mechanism, personalized incentive and model adaptive updating are realized, user health management compliance and blood pressure control effect are effectively improved, and an intelligent solution is provided for health management of a chronic disease population such as hypertension.