163 results about "Chronic disorders" patented technology

The invention discloses a chronic disease early detection method and system based on a multi-modal large model, and relates to the technical field of intelligent medical treatment and artificial intelligence, and the method comprises the steps: obtaining a multi-modal data stream of a target user in a target time window from a pathology database, and generating an original multi-modal data set; performing timestamp unification and numerical value standardization processing on the original multi-modal data set to obtain a time sequence feature sequence; inputting the time sequence feature sequence to the multi-modal large model to obtain an abnormal symptom feature; calculating the similarity between the abnormal symptom features and feature vectors of marked cases in a historical case library, and determining matched cases; a diagnosis result and a development process of the matched case are extracted, a disease risk level and a development trend corresponding to the original multi-modal data set are determined in combination with the medical knowledge graph, and a pathology assessment result is obtained; and generating an early warning signal containing the risk type and the intervention suggestion according to the pathological assessment result. By implementing the application, the accuracy of early detection of chronic diseases can be improved.

The invention discloses a patient medication risk early warning method, and relates to the technical field of medication risk evaluation. The method comprises the following steps: acquiring multi-dimensional data such as age, past medical history, allergy history, clinical examination data and prescription data of a patient, extracting chronic disease diagnosis information from the multi-dimensional data, and carrying out standardized coding to generate a common disease combination tag; calling a preset disease weight system, and calculating a common disease severity score in combination with an age factor; extracting metabolism-related indexes to construct a drug metabolism capability feature vector of the patient; mapping the drug combination, the co-disease tag and the metabolic feature vector to a clinical drug knowledge base to generate a comprehensive conflict detection result; and generating corresponding early warning information based on the conflict detection result and a preset early warning rule base, wherein the rule base comprises a mapping relationship between the conflict type and the early warning level. According to the method, drug use risk early warning can be realized according to the multi-disease coexistence state of the patient, and the drug use safety is improved.

The invention discloses an illness state tracking management system and method based on a lingual face diagnosis instrument, and belongs to the technical field of traditional Chinese medicine tongue diagnosis and modern information technology fusion. The system comprises a multi-modal data acquisition module, a data processing and analysis module and an augmented reality visualization module; according to the method, illness state tracking is achieved through the steps of multi-modal data acquisition, data preprocessing and feature fusion, personalized digital twinborn model establishment, augmented reality visualization presentation and the like, multi-dimensional data such as tongue picture macroscopic features and tongue surface microorganism distribution can be integrated, dynamic association between the data is revealed, the health state and the intervention effect are visually displayed, and the method is suitable for being popularized and applied. The method is suitable for the fields of traditional Chinese medicine health management and chronic disease monitoring.

The invention belongs to the field of nano-enzyme preparation and biological application, and particularly relates to a preparation method and application of M2 type macrophage membrane coated FeMn diatomic nano-enzyme. The preparation method comprises the following steps: by taking nitrogen-doped carbon (BANT) as a carrier, loading Fe and Mn diatoms, synthesizing a novel nano material FeMnDA / BCNT diatomic nano-enzyme, extracting a macrophage membrane from natural macrophages, and coating the FeMnDA / BCNT nano material with the macrophage membrane to finally form M2 type macrophage membrane coated nano-particles, namely [MM] FeMnDA / BCNT. The [MM] FeMnDA / BCNT nano-enzyme prepared by the invention has good biological safety and simulated SOD and CAT enzyme activity, and the expression of inflammatory factors is reduced by removing excessive ROS (reactive oxygen species) in the cartilage cells induced by H2O2, so that the damage of oxidative stress to the cartilage cells is inhibited. The [MM] FeMnDA / BCNT nano-enzyme is not used for treating OA yet, so that a scientific basis is provided for further application and expansion of the diatomic nano-enzyme, and an effective strategy and a new thought are provided for treating osteoarthritis and chronic diseases related to oxidative stress in the future.

The invention relates to the technical field of chronic disease examination, in particular to an AI-based chronic disease risk prediction and early intervention method, which comprises the following steps of: acquiring physiological data, living habit data, environmental data and historical health data of a user through various devices and platforms, and then preprocessing the acquired data to obtain an AI-based chronic disease risk prediction and early intervention result; the method includes the steps of data cleaning, missing value filling and standardization, then conducting feature selection through a principal component analysis method, extracting key features related to chronic disease risks, building a chronic disease risk prediction model through an improved random forest algorithm on the basis of the key features, and finally predicting the chronic disease risks in combination with a model prediction result. Personalized intervention suggestions are provided for the user, and diet adjustment, exercise plans, drug prevention, regular physical examination and other aspects are covered; according to the method, high-risk people with chronic diseases can be accurately identified in an early stage, and accurate risk prediction and personalized intervention strategies are provided, so that occurrence and development of the chronic diseases are effectively prevented.

The invention belongs to the technical field of traditional Chinese medicines, and particularly relates to a method for extracting pogonatherum crinitum with anti-inflammatory activity and a compound and an extract thereof. The invention firstly provides a preparation method and quality classification recommendation of the pogonatherum crinitum extract with anti-inflammatory activity and the pogonatherum crinitum formula extract. The method provided by the invention is beneficial to standardized screening of the medicinal material (raw material) of the pogonatherum crinitum from the source, and in addition, based on quality detection indexes of efficacy and safety, the purpose of accurately guiding medication for different patients (chronic diseases or acute attack of diseases) can be achieved.

The invention discloses a personalized recipe recommendation system, a recommendation method and an interaction system, and relates to the technical field of diet recommendation, the system comprises a user data collection module, a food image recognition module and a personalized recommendation module, the user data collection module is used for obtaining health data and diet preference data of a user, the health data comprises basic information, health conditions and exercise habits, the basic information comprises regions, the health conditions comprise allergen lists, chronic diseases and diet targets, and the food image recognition module is used for obtaining food photos uploaded by a user, recognizing the food photos to obtain food recognition results and sending the food recognition results to the user. The food identification result comprises a food type corresponding to the food photo, and the personalized recommendation module is used for determining a recommended recipe recommended to the user based on the health data, the diet preference data and the food identification result. By considering more comprehensive influence factors, a more suitable and personalized recipe can be recommended to the user.

Embodiments of the present invention generally relate to novel immunostimulatory compositions of use to stimulate non-specific immune responses in a subject. In certain embodiments, immunogenic compositions disclosed herein can be directed to use in the eye of a subject. In some embodiments, the immunogenic compositions disclosed herein enhance non-specific immune responses in the eye of a subject to treat or reduce the risk of onset of an eye condition. In other embodiments, compositions disclosed herein can be used to treat eye infections due to a microorganism, tumors of the eye, as well as, chronic wounds or infections of the eye. In some embodiments, immunogenic ocular formulations disclosed herein include combinations of agents for prolonged exposure to the eye for improved outcomes.

The invention provides a teenager disease risk dynamic prediction method and system, and the method comprises the steps: obtaining an electronic health record and subcutaneous metabolite dynamic concentration data of a teenager, and the electronic health record comprises chronic disease related information, a genetic risk value and historical diagnosis data; performing multi-modal fusion on the metabolite concentration data, the genetic risk value and the historical diagnosis data according to a time axis to form a mixed time sequence information set; adaptive optimization is carried out on the mixed time sequence information set, and the collaborative change trend of metabolite fluctuation characteristics and genetic characteristics is identified from the optimized mixed time sequence information set; generating a dynamic graph by adopting a graph neural network based on the collaborative change trend; and tracking an abnormal conduction chain of a metabolic pathway in the dynamic map in real time, and calculating a risk probability matched with the growth stage of the teenagers according to the abnormal conduction chain and chronic disease related information. The dynamic prediction precision and timeliness of the chronic disease risk of teenagers are improved.

The invention relates to a chronic disease risk automatic early warning method based on multi-modal data analysis, and belongs to the technical field of intelligent medical treatment and health management. The method comprises the following steps: acquiring multi-modal health data of a user in real time through a multi-modal data acquisition module, and performing feature extraction to obtain individual multi-dimensional health features; based on the features, a convolutional neural network and a graph neural network are called to be combined with the chronic disease type to construct a chronic disease risk prediction model, and potential chronic disease risks are judged through leaving-one verification; analyzing a risk level according to the risk result and generating early warning information; and carrying out risk detection based on the evaluation index, and if the risk is higher than a model set threshold, sending a risk early warning signal through the terminal device. Accurate assessment and personalized early warning of chronic disease risks are realized.

The invention provides a chronic patient symptom management method and system based on symptom network analysis, and relates to the technical field of data processing. The method comprises the following steps: collecting a chronic disease patient symptom evaluation data set; a principal component analysis method and a maximum variance orthogonal rotation method are combined, and a symptom group in the symptom evaluation data set of the chronic disease patient is extracted; constructing a symptom network by taking the symptoms as nodes and the relationship between the symptoms as edges; core symptoms and bridge symptoms are identified by using synchronous network analysis; constructing a dynamic symptom network based on a cross-lag network model; the influence factors are introduced into the dynamic symptom network, subgroup analysis is carried out on the dynamic symptom network, and core influence factors are identified; constructing a chronic disease development trend prediction model based on LSTM; collecting clinical symptoms and clinical indexes of the chronic disease patient; extracting core influence factors; and predicting and outputting the development result of the chronic disease patient through the LSTM-based chronic disease development trend prediction model.

The present invention relates to the technical field of anti-aging compositions and particularly to an anti-aging composition and use thereof, and an anti-aging capsule and use thereof. The present invention provides an anti-aging composition comprising the following components: 20-2000 parts by mass of calcium α-ketoglutarate, 20-100 parts by mass of quercetin, 20-2000 parts by mass of trans-resveratrol, 20-100 parts by mass of calendulin, and 0.02-1.8 parts by mass of spermidine. The composition of the present invention has a multi-target synergistic effect on 12 major causes of aging and can safely and effectively reverse aging, enhance the physiological function, reduce the risk of chronic diseases, and improve the quality of life. The components of the composition are safe, and are carefully selected natural food ingredients; therefore, the composition is safe and reliable, and has no side effects with long-term use. The composition is prepared in an oral dosage form, so that the composition is convenient to administer, which improves patient compliance; moreover, the mode of application of the composition can be expanded.

The invention discloses a chronic disease prognosis method, a chronic disease prognosis model, a chronic disease prognosis device, computer equipment and a storage medium. The chronic disease prognosis method comprises the following steps: acquiring a medical image and medical data of a to-be-prognosed person; in the medical image, deep learning features and radiomics features are extracted; determining shared features and unique features of the deep learning features and the radiomics features in the deep learning features and the radiomics features; performing fusion processing based on the shared features and the unique features to obtain target fusion features; based on the target fusion features and the medical data, constructing a multi-omics graph; and performing prognosis treatment based on the multiple omics graphs to obtain a prognosis result of the to-be-prognosed person. Through the target fusion features and data of different modalities such as medical data, the constructed multi-omics graph can enhance the modeling ability of the model for complex pathological information on the basis of improving expression complementarity among different modalities, so that the accuracy and robustness of a prognosis prediction result are remarkably improved.

The invention relates to the technical field of intelligent monitoring medical treatment of senile chronic diseases, and discloses a hypertensive brain injury early-stage monitoring and evaluation system and method based on multi-mode MRI data, and the system comprises a data collection module, a data preprocessing module, a virtual elastography analysis module, a microcirculation analysis module, a feature extraction and prediction model module, and a data visualization and report module. The method further comprises the steps of multi-modal MRI data collection, data processing, virtual elastic tensor calculation, lymphatic-like system function analysis, image feature extraction, prediction model construction, quantitative report generation and personalized suggestion providing. According to the method, virtual elastography analysis and microcirculation analysis technologies are combined, multi-dimensional data such as cerebrovascular elasticity modulus, blood flow velocity and lymphatic-like system function indexes are integrated, more comprehensive hypertension cerebrovascular injury assessment is provided, and in addition, the accuracy of hypertension cerebrovascular injury assessment is improved by combining a machine learning algorithm and radiomics characteristics. And a high-precision cerebrovascular injury prediction model is constructed.

The invention discloses an intelligent monitoring wrist strap for plateau adaptive sports and a system thereof. The intelligent monitoring wrist strap comprises a monitoring main body, a left wrist strap and a right wrist strap, one side of the monitoring main body is provided with a power supply lamp, a gait monitoring lamp and an osteoporosis early warning lamp, the other side of the monitoring main body is provided with a charging port, and the monitoring main body is internally provided with a micro-processing chip, an air pressure sensor, a temperature sensor, an ultraviolet sensor and an alarm microphone; an osteoporosis early warning module is arranged at the lower part of the monitoring main body, a display screen is arranged at the upper part of the monitoring main body, and an air pressure sensor, a temperature sensor, a blood oxygen detection module, a heart rate detection module, a gait monitoring module, an osteoporosis early warning module and a wireless transmission module are integrated on the micro-processing chip. According to the plateau environment characteristics and the skeletal system chronic disease risk, the motion state and health data of the wearer are dynamically monitored, and individualized motion guidance and early warning information is generated, so that the risk of plateau crowds in the motion process is reduced, and the individualized health management level is improved.

This invention relates to a body fluid-based detection device for chronic diseases, which belongs to the field of medical devices. The device includes a body fluid detection fluid channel, a detection probe, a data processing module, and a data display module. The detection probe is disposed within the body fluid detection fluid channel, and the detection probe is connected to the data processing module, which is connected to the data display module. The body fluid detection fluid channels include: a body fluid ion detection channel, a body fluid pH detection channel, a body fluid hydrodynamic characteristic detection channel, a channel for detecting the content of soluble gases such as oxygen, a channel for detecting the physical characteristics of white blood cells and tumor cells, a channel for detecting the electrical characteristics of white blood cells and tumor cells, a channel for detecting the physical characteristics of red blood cells, a channel for detecting the electrical characteristics of red blood cells, a channel for detecting the electrical characteristics of separated body fluids, and a channel for detecting anticoagulation and fibrinolysis functions. This invention enables highly sensitive, non-invasive testing, providing a powerful tool for the early diagnosis of chronic diseases.

Disclosed are compositions and methods for treating cortical hyperexcitability and reversing stroke-induced changes in gene expression and protein expression. For example, the methods can comprise administering vandefitemcel to a region of the brain of a subject to reduce chronic cortical hyperexcitability in the subject. Also disclosed are methods and blood biomarker panels for assessing an efficacy of a stem cell treatment for a chronic condition caused by stroke.

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.

Provided are therapies and regimens for treating chronic diseases, disorders and conditions having multiple intervention targets and / or treatment targets. The therapies include administration of a combination regimen and administration of an alternating combination regimen. The regimen comprises a combination of pharmaceutical and / or non-pharmaceutical treatments.

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.

AI-Driven Multimodal Framework Computer for Early Detection and Remote Monitoring of Chronic Disease

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.

To provide compositions and methods for treating and preventing cognitive impairment, movement disorders, chronic pain, depression, decreased appetite, and addiction.SOLUTION: Also provided are methods for the diagnosis and monitoring of cognitive impairment, movement disorders, chronic pain, depression, decreased appetite, addiction, and other conditions such as convulsion or seizure. These compositions comprise fatty acid compounds and salts thereof which may be administered either alone, in combination with other medicaments, or as part of various treatment regimens. The provided compositions are effective for modulating markers, including, but not limited to, behavior associated with cognitive impairment, movement disorders, chronic pain, depression, decreased appetite, opiate addiction, and other related conditions. Methods for administering the compositions are also provided.SELECTED DRAWING: Figure 1

Systems and methods for monitoring patients with multiple chronic diseases are described. A system may include a health status monitor that receives diagnostic data including physiological signals sensed from a patient. The system may produce at least a first risk indication of the patient developing a first disease and a second risk indication of the patient developing a different second disease. The system may detect the first and second diseases from the physiological signals, and generate a composite health status indicator using the detections of the first and second diseases and the first and second risk indications. An alert of worsening health status may be generated if the composite detection score exceeds an alert threshold.

The disclosure generally relates to ready to drink electrolyte solution. In particular the disclosure relates to a ready to drink electrolyte solution comprising isomaltulose, glucose, sodium ions, chromium ions and water; wherein the glucose concentration is greater than or equal to 0.9g / 100ml, and wherein the theoretical glycemic index of the solution is equal to or less than 55. The disclosure also relates to a method of manufacturing said ready to drink electrolyte solution. The disclosure also relates to a method of rehydrating a patient suffering from cancer, diabetes, acute or chronic illness, or a gastrointestinal disorder by using said ready to drink electrolyte solution.

The invention relates to the field of computer vision, artificial intelligence and medical image analysis, in particular to a disease prediction method for supervising multi-omics tensor fusion, and the method comprises the steps: obtaining a tensor covariance among multi-omics data according to the processed multi-omics data, and defining a target function of tensor canonical correlation analysis; according to the tensor covariance and the target function, introducing structured sparse constraint and disease supervision information, and constructing a multi-modal image correlation analysis model based on tensor; solving the multi-modal image correlation analysis model by using an alternating iteration method to obtain a typical weight of each omics data; and predicting the disease according to the typical weight of each piece of omics data. According to the supervised multi-omics data fusion method based on the tensor, high-order related information can be effectively mined, the classification accuracy of chronic diseases is improved, and powerful technical support is provided for clinical diagnosis.

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.

Therapies and regimens for treating chronic diseases, disorders, and conditions that have a plurality of intervention targets and / or therapeutic targets are provided. Therapies include administration combinatorial regimens, and administration of rotational combinatorial regimens. The regimens comprise combinations of drugs and / or non-drug treatments.

The invention belongs to the technical field of biomedical engineering and physical therapy, and discloses a method for improving hypertension, hyperglycemia and hyperlipidemia based on negative oxygen thermomagnetism. The method comprises the following specific steps: S1, professional medical consultation and evaluation; S2, selection of qualified treatment equipment; S3, setting of personalized treatment parameters; S4, preparation of a safe and comfortable treatment environment; through cooperative use of the thermomagnetic therapeutic apparatus and the negative oxygen ion generator, remarkable benefits can be brought to patients with'three highs' chronic diseases, the thermomagnetic therapeutic apparatus is reasonably arranged and can accurately act on a target part, a proper gap is kept, discomfort and effect disintegration are avoided, the negative oxygen ion generator and the thermomagnetic therapeutic apparatus are synchronously started, and the therapeutic effect is good. The device is started after confirming that the device is in a good state, a treatment area can be filled with negative oxygen ions, and a beneficial treatment environment is created.

The invention relates to the field of protein, in particular to a milk-derived peptide APPSPIGVF with antihypertensive activity as well as a preparation method and application of the milk-derived peptide APPSPIGVF, and the amino acid sequence of the milk-derived peptide APPSPIGVF is Ala-Pro-Pro-Ser-Pro-Ile-Gly-Val-Phe. An angiotensin converting enzyme activity inhibition experiment, an alpha-amylase activity inhibition experiment and a dipeptidyl peptidase-4 activity inhibition experiment prove that the milk-derived peptide APPSPIGVF has very good functions of resisting hypertension and reducing blood sugar. The milk-derived peptide APPSPIGVF disclosed by the invention has an anti-hypertension capability, and can be used for remarkably inhibiting the activity of alpha-amylase and dipeptidyl peptidase-4, improving the capability of resisting chronic diseases such as hypertension and hyperglycemia of an organism, reducing the morbidity of the organism and improving the quality of life; the method has very important significance for developing food, health care products and medicines with the functions of resisting hypertension and reducing blood sugar.