77 results about "Disease management" patented technology

The invention discloses a grape disease recognition and early warning method based on the Internet of Things, and relates to the technical field of plant disease recognition, image acquisition equipment and environment sensing nodes are arranged in a vineyard, and leaf images and corresponding temperature and humidity, illumination and soil moisture parameters are obtained; inputting the image into a neural network fusing dilated convolution and a residual attention mechanism, realizing extraction of a disease spot region and a disease spot variation feature, and generating a preliminary recognition result; constructing a multi-factor evolution sample set in combination with the recognition result and the environment state of the time node; constructing a space-time correlation graph model based on a graph neural network, estimating a disease propagation risk path and a diffusion probability, and performing early warning judgment at a gateway end through a multi-factor gating discrimination algorithm; the method disclosed by the invention is high in recognition precision and strong in response timeliness, has adaptive prediction and targeted treatment capabilities, and remarkably improves the intelligence and precision level of grape disease management.

A modular disease management system may provide medication management for a patient. The system may include a base configured to at least partially couple to a tissue site of a patient. The system may further include a medication bladder coupled to the base and configured to maintain medication. The system may further include a medication pump coupled to the medication bladder, comprising a conduit and muscle wire-actuated plungers, wherein the medication pump is configured to cause the medication to flow from the medication bladder through the conduit. The system may further include a cannula insertion device coupled to the medication pump via the conduit, wherein the cannula insertion device comprises a cannula, a spring, and a triggering component, wherein the cannula insertion device is configured to insert the cannula into the tissue site of the patient by actuation of the spring released by the triggering component.

The invention relates to the technical field of peanut disease intelligent monitoring scheme design, in particular to a peanut disease intelligent monitoring method and system and electronic equipment. RGB and near-infrared images are synchronously acquired through a dual-channel acquisition device, after illumination compensation, defogging and geometric correction, multi-scale features are extracted by using a transfer learning optimized Eff cientNet-B4 network, and spectral information is fused by using a dual-path CBAM attention mechanism to generate a disease sensitive feature vector. The cascade classifier realizes disease type identification and severity grading based on ResNet-34 and a random forest model, and predicts a disease development trend in combination with an LSTM time sequence model. And integrating a U-Net segmentation network to generate a visual report, and associating an expert knowledge base to output a prevention and treatment scheme. According to the method, the problems of low detection precision, poor model generalization ability and insufficient decision support of a traditional method are solved, and an efficient and accurate disease management tool is provided for peanut planting.

The disease management system can determine a measurement of one or more physiological parameters and can determine a disease event based on the one or more measurements. Furthermore, the disease management system can determine a pose of an individual using a pose sensor. Based on an identification of a disease event and a determination that the pose of the individual corresponds to a first pose, the disease management system can cause at least one of an audible, visual, or vibratory alarm. Based on an identification of a disease event and a determination that the pose of the individual does not correspond to the first pose, the disease management system can cause administration of a medication to the individual.

The invention provides a big data resource service-based patient hierarchical intervention method and system, which are applied to the technical field of medical information, and are used for acquiring full-cycle health data of a patient, generating and updating a patient disease course trajectory sequence, analyzing health index change and trend under short, medium and long time scales, and calculating health scores and steady-state coefficients, so as to realize hierarchical intervention of the patient. A layered intervention scheme is generated, detection and personalized intervention of the health state of the patient are achieved, the timeliness and pertinence of medical intervention can be improved, and the disease management effect is optimized.

The invention discloses a preventive disease management system based on a multi-mode large model, and relates to the technical field of medical management, the preventive disease management system collects user lingual surface images and on-line questionnaire data to obtain traditional Chinese medicine physique, health symptoms and personal basic information, and the image data is subjected to preprocessing technologies such as filtering and enhancing to obtain the preventive disease information; key tongue picture characteristics such as tongue color, tongue coating and tongue shape are extracted; performing comprehensive health assessment on the multi-modal data by using a pre-trained deep learning model, and preliminarily judging viscera functions, meridian qi and blood states and potential health risks of the user; an evaluation result is fused with auxiliary information such as original data, a current season and a regional environment, and a personalized health planning scheme covering diet, work and rest and exercise suggestions is generated through a multi-modal large model; an AI-AGENT-based intelligent agent is adopted to execute a health management task, and real-time data feedback is utilized to realize continuous optimization of dynamic planning; and the problems of insufficient intelligence and individuation of the traditional disease prevention management are solved.

The invention relates to a chronic disease stage prediction method and device based on a dynamic physiological time sequence and wearable equipment, and the method comprises the steps: obtaining a historical health state pre-stored in a resident holographic health record of a target resident; collecting dynamic physiological time sequence data of a current time window of the target resident; and inputting the dynamic physiological time sequence data and the historical health state into a pre-trained health management vertical class large model, and generating a chronic disease outcome risk prediction value, so as to achieve the purpose of constructing a chronic disease accurate prevention and control system from static diagnosis to dynamic prediction and from single disease management to common disease evolution comprehensive evaluation.

The invention discloses a co-disease collaborative identification and risk early warning method based on multi-modal data and an adaptive large model. The method comprises the following steps: collecting multi-modal medical data; carrying out preprocessing and feature extraction on the multi-modal medical data; based on the fine-tuned adaptive large model, deep interactive fusion is carried out on the extracted multi-modal features, potential association among different data sources and mutual influence and synergistic effect among various diseases are learned, and combined judgment and future onset risk prediction of the current common disease state of the patient are output; determining a specific co-disease combination and a co-disease risk level based on the combined judgment of the current co-disease state of the patient and a future onset risk prediction result; and when the determined co-disease risk level is equal to or higher than an early warning threshold, generating a collaborative early warning and intervention suggestion. According to the invention, a cross-modal and cross-disease feature fusion technology can be utilized to realize conjoint analysis and comprehensive risk early warning of multiple disease states of the patient, and the co-disease management efficiency and the clinical auxiliary decision-making level are significantly improved.

This invention relates to the field of medical information technology, and in particular to a method and system for generating clinical multi-agent task sequences. The method includes the following steps: acquiring and structuring core clinical objectives, which include patient basic information, clinical indicators, and disease management goals; decomposing the core clinical objectives into several structured sub-objectives with dependencies and execution order based on a clinical pathway template library and a medical knowledge graph; for each structured sub-objective, splitting it into multiple minimum executable clinical sub-tasks according to a clinical sub-task decomposition rule library, and defining the execution subject type, input / output, and dependencies for each clinical sub-task to construct a sub-task-level directed acyclic graph; integrating the sub-task-level directed acyclic graphs, identifying dependencies across structured sub-objectives, generating a global clinical task directed acyclic graph, and performing dependency verification and clinical logic consistency verification.

The present invention relates to a depression disease management system based on targeted metabolomics and machine learning models. The system comprises an intelligent diagnostic model module, a differential diagnostic model module, a hierarchical diagnostic model module, a companion diagnostic model module, a treatment endpoint outcome prediction model module, a relapse prediction model module, and a comprehensive judgment module. The system provided by the present invention can address current clinical challenges in depression diagnosis, treatment outcomes, and relapse, enabling accurate diagnosis, timely treatment, and improving the mental health of the general population.

Carrier-based agricultural biofertilizer composition comprising an isolated Trichoderma harzianum CGAJ2T-3 of deposit accession number KY495199 and an agriculturally acceptable formulation comprising a stabilizer (e.g., carboxymethyl cellulose (CMC)). The strain may be isolated from the vegetable rhizosphere and rhizoplane from Yola, Nigeria; e.g., by serial dilution and spread plate method. The formulation may comprise carriers selected from: talc, vermiculite, peat, compost. Also disclosed method for preparing carrier-based agricultural biofertilizer composition, comprising: preparing nutrient medium comprising carbon and nitrogen sources (e.g., sucrose, glucose, malt extract, peptone); inoculating medium with a pure culture of Trichoderma harzianum CGAJ2T-3; performing fermentation; extracting fungal spores and mycelia from the broth (e.g., filtration, centrifugation); employing a solid substrate fermentation technique (e.g., utilising rice bran and wheat straw) to attain 3.0×10^9 CFU / g; diluting formulation to 3.0×10^9 CFU / mL within talc powder-based or liquid formulation; and administering formulation to soil (e.g., spraying onto foliage or root zones).

Respiratory diseases such as asthma and chronic obstructive pulmonary disease (COPD) are commonly treated with β2-adrenergic receptor agonists that activate intracellular signaling pathways to induce airway relaxation. However, current therapies are limited by progressive receptor desensitization, or tachyphylaxis, largely attributed to β-arrestin recruitment. This desensitization reduces therapeutic efficacy over time and complicates long-term disease management, resulting in suboptimal outcomes for patients who rely on bronchodilators for daily symptom control. Disclosed herein are pharmaceutical compositions comprising biased agonists and their methods of use, which minimally activate recruitment of beta-arrestin2.

The invention provides a method for identifying the iridovirus damage grade of micropterus salmoides based on machine vision assistance, and belongs to the technical field of aquatic product infection identification. According to the method, the damage degree of micropterus salmoides infected with iridovirus is measured in a standardized mode, early-stage micropterus salmoides body surface damage data are analyzed based on visual machine assistance, body surface damage areas are divided, the body surface damage number and damage area are measured, individual comprehensive damage scores are calculated through data standardization and PCA dimension reduction, and body surface damage grades are constructed. And a scientific basis is provided for disease management of the micropterus salmoides.

The invention discloses a rare child disease management method and system based on artificial intelligence and electronic equipment, and relates to the technical field of artificial intelligence and health cross, and the method comprises the steps: constructing and training a first preset deep learning model, and obtaining an intelligent question and answer model; and acquiring rare disease associated data of the rare sick child patient in a question and answer mode by using the intelligent question and answer model, and analyzing the rare disease associated data to generate a corresponding personalized health management scheme. According to the method, the intelligent question and answer model is obtained by constructing and training the first preset deep learning model, the problem that in the prior art, a targeted efficient tool is lacked to obtain the associated data of the rare sick children is solved, and scattered data can be effectively integrated. The intelligent question and answer model is used for obtaining data in a question and answer mode and analyzing the data, then the personalized health management scheme is generated, the defects that a traditional method depends on artificial experience and is difficult to meet the requirement for rapidly and accurately formulating the scheme are overcome, and the scientificity and accuracy of the health management scheme are improved.

An apparatus for generating personalized risk assessments for neurodegenerative diseases includes a computing device that receives user data containing genetic and medical information. It processes the data to create genotype identification and gene detection modules, identifying user genotypes and relevant genetic markers. The user's mitochondrial haplogroup is examined to refine the assessment. A risk calculation module employs machine learning to weigh genetic variants against population-based data, calculating a polygenic risk score (PRS). The PRS forms a personalized risk profile, displayed through a visual interface. The disclosed systems offer a comprehensive approach to accurate risk assessment, enabling targeted interventions and informed decision-making in neurodegenerative disease management.

A system uses a large language model (LLM) to implement a controlled artificial intelligence chat environment. The system may control interaction with the LLM using prompt templates that may be selected, customized, and / or modified based on information known about the user with whom the LLM will be interacting. Further, the system may evaluate output of the LLM to make changes to the LLM, the prompt templates, and so on. In some implementations, the system may use evaluation training data to adapt and fine-tune the LLM and / or another language model to evaluate output of the LLM in order to evaluate the efficacy of the chronic condition and / or disease management coaching path(s), and make improvements to the online or offline implementation of the language model in the future.

A modular disease management system may provide medication management for a patient. The system may include a base configured to at least partially couple to a tissue site of a patient. The system may further include a medication bladder coupled to the base and configured to maintain medication. The system may further include a medication pump coupled to the medication bladder, comprising a conduit and muscle wire-actuated plungers, wherein the medication pump is configured to cause the medication to flow from the medication bladder through the conduit. The system may further include a cannula insertion device coupled to the medication pump via the conduit, wherein the cannula insertion device comprises a cannula, a spring, and a triggering component, wherein the cannula insertion device is configured to insert the cannula into the tissue site of the patient by actuation of the spring released by the triggering component.

The invention discloses a preparation method of a nano pesticide for enhancing the bactericidal activity of sclerotinia sclerotiorum. The ROS response type PYD (at) OxbCD nano-particles are successfully prepared by packaging the PYD in an Ox-bCD nano-carrier, so that the ROS response type PYD (at) OxbCD nano-particles are obtained. The system has high drug loading capacity and encapsulation efficiency, and shows excellent response characteristic to active oxygen. Under the condition of H2O2, PYD can be intelligently released from the nanoparticles, and compared with a PYD suspending agent, the bactericidal activity on sclerotinia sclerotiorum is remarkably enhanced. In-vitro experiments show that the PYD (at) OxbCD NPs can inhibit the growth of mycelia and reduce the pathogenicity and re-infection potential. In soybean plants, ROS at scabs triggers PYD release, the system shows an excellent prevention and treatment effect on sclerotiniose, and meanwhile, inhibition on seed growth is relieved. Therefore, as an efficient pesticide delivery and disease management strategy, the PYD (at) OxbCD NPs has the advantages of precise targeting and reduction of human and livestock environmental risks.

The present application relates to the medical health information technology field, especially to a patient disease evolution relationship prediction method and system based on a time series graph neural network, comprising: step S01, obtaining offline diagnosis data and online diagnosis data, and establishing first medical record data information and second medical record data information based on the offline diagnosis data and the online diagnosis data; step S02, arranging the first medical record data information and the second medical record data information according to time sequence, obtaining comprehensive medical record data information, analyzing all medical record data, and obtaining target disease medical record data information to establish target disease medical record data information. The present application can fill in the "silent period" caused by a single data source by integrating offline diagnosis and online diagnosis, ensure the completeness of patient treatment records, and improve the continuity of disease management.

Diabetes management systems may include a glucose monitoring device adapted to transmit glucose data and at least one accessory in communication with the glucose monitoring device and adapted to provide at least one alarm or alert based on the glucose data.

The invention relates to the technical field of medical image analysis, in particular to a chronic obstructive pulmonary disease severity AI grading method based on a CT image, which comprises an image acquisition module, a feature extraction module, a grading evaluation module and an anomaly detection module. A chest image of a patient is obtained through CT scanning, lesion features are extracted through deep learning, a severity index is calculated in combination with a grading standard, and abnormal conditions are detected. According to the method, the lesion feature extraction precision and the diagnosis efficiency are improved, the manual error is reduced, the diagnosis process of the chronic obstructive pulmonary disease is optimized, the comprehensive assessment capability of complex disease conditions is enhanced, and refined support is provided for disease management.

The invention provides an application of an Oncostatin-M concentration level in diagnosis or auxiliary diagnosis of obesity-related chronic kidney diseases. By combining Mendel randomization (MR) analysis with clinical research, single cell sequencing analysis and animal experiments, the concentration of Oncostatin-M in a serum sample is determined, and the Oncostatin-M can be used as a diagnostic marker of obesity-related kidney diseases (CKD) and is used for clinical auxiliary diagnosis and disease management.

The invention provides a community old-age care chronic disease management interaction method and system fusing local medical propaganda knowledge, and the method comprises the steps: carrying out the statistics of basic sign data corresponding to each chronic disease patient in a community range, and determining the chronic disease development trend of the community range, according to medical conditions of a local hospital and local conditions of a community range, constructing propaganda knowledge corresponding to each chronic disease category, transmitting the propaganda knowledge to a community terminal for display, according to the propaganda knowledge and basic sign data corresponding to each chronic disease patient, constructing a health maintenance scheme of each chronic disease patient, and transmitting the health maintenance scheme to the community terminal for display. According to the method, health questions uploaded by chronic disease patients are obtained, corresponding health maintenance schemes are uploaded to big data to generate intelligent replies and are transmitted to a community terminal for display, local medical conditions and local environmental conditions are integrated into chronic disease propaganda, old people are helped to manage their bodies in daily life, and the health management efficiency is improved. The harm of chronic diseases to the health of old people is effectively relieved.

The present application relates to the technical field of rheumatism and immunity disease management, and particularly relates to a remote intelligent management method and system for rheumatism and immunity patients. Clinical indexes, environmental monitoring and life behavior data are acquired to construct a multi-element characteristic vector, which is input into a deep learning network to obtain a disease activity prediction result; the prediction result and the characteristic vector are input into a recurrence risk assessment network containing a collaborative constraint layer, and a consistency correction is performed based on a medical prior constraint to obtain a risk assessment result; a counterfactual replacement is performed on the characteristic vector through a causal inference algorithm, the risk assessment network is input to obtain a counterfactual prediction value, a difference value is calculated as a causal contribution degree, risk driving factors are screened, and quantitative intervention suggestions are generated; and operable early warning information is generated based on the driving factors and the intervention suggestions. The present application improves the accuracy of recurrence risk prediction and the pertinence of intervention suggestions.

The present application relates to the technical field of spinal disease management, and specifically discloses a spinal degenerative disease management path method and system based on big data, which comprises the following steps: collecting multi-source heterogeneous data of spinal degenerative disease in different departments and preprocessing, obtaining a standard data set of multi-source heterogeneous data and extracting structural features, generating a standard structural representation of spinal degenerative disease, setting an adaptive screening mechanism, screening and classifying the standard data set of spinal degenerative disease in combination with the standard structural representation to obtain a classified data set, constructing a knowledge graph of spinal degenerative disease, integrating new patient feature data, constructing a time series monitoring network model for reasoning and prediction, obtaining a patient disease progression trend and a potential risk vector, generating a personalized management path based on the disease progression trend and the potential risk vector output by the time series monitoring network model, which is conducive to improving the accuracy of spinal degenerative disease diagnosis and treatment and providing an adaptive disease management path scheme.

The embodiment of the present specification provides an intelligent disease management method, system and storage medium. The intelligent disease management method comprises: in response to detecting that a target patient meets a view display condition, acquiring patient information of the target patient, the patient information comprising a target disease type, a target disease course and examination information; determining a patient view template based on the target disease type and the target disease course; and generating a target patient view of the target patient based on the patient view template and the examination information, and displaying the target patient view via a first terminal.

This invention discloses the application of total flavonoids from sea buckthorn leaves in the preparation of drugs for treating ulcerative colitis, relating to the pharmaceutical field. Animal experiments of this invention have demonstrated that total flavonoids from sea buckthorn leaves can effectively alleviate clinical symptoms and pathological damage of ulcerative colitis; improve intestinal barrier damage in mice with ulcerative colitis; improve the inflammatory response in mice with ulcerative colitis; improve the decreased secretion of short-chain fatty acids in mice with ulcerative colitis; and regulate the abundance and structure of intestinal flora in mice with ulcerative colitis. It shows promise for developing new anti-ulcerative colitis drugs. Therefore, sea buckthorn leaves can be transformed from waste into a valuable resource, providing solid preclinical research evidence for the subsequent development of special medical foods, health products, or functional teas with sea buckthorn leaf flavonoids as the core component, offering daily nutritional support and disease management for patients with ulcerative colitis.

Described herein are methods and compositions for treatment of diabetes and / or obesity. Bitter oligopeptide molecules formulated for enteric delivery modulate signals involving receptors facing the lumen of the gastrointestinal tract, said signaling related hormones such as glucagon-like peptide-1 (GLP-1) and peptide tyrosine-tyrosine (PYY) that involve inhibition of gastric emptying and appetite. As a novel way to treat diabetes with limited adverse effects the described invention uses body's own endocrine system to treat diabetes, which is an advantage over current therapies that may simply provide disease management without cure or require more radical approaches such as surgical intervention.

The invention discloses a differential diagnosis model (BC-ACTUAL) for Crohn's disease and gastrointestinal behcet disease and application of the differential diagnosis model. Specifically, the invention discloses application of an index combination (Age, C4, TP, UA, ALP and LDH) or a reagent for detecting the index combination in preparation of a product for differential diagnosis of Crohn's disease and gastrointestinal behcet disease. The invention also discloses a logistic regression model established by using the index combination and a scoring system. The model has the characteristics of high sensitivity, good specificity, economy, high efficiency and convenience, can accurately identify and diagnose the Crohn's disease and the gastrointestinal Behcet's disease, is suitable for clinically identifying the Crohn's disease and the gastrointestinal Behcet's disease, helps a clinician to preliminarily distinguish the Crohn's disease and the gastrointestinal Behcet's disease under the condition that only common laboratory indexes are used, and improves the detection accuracy of the Crohn's disease and the gastrointestinal Behcet's disease. The situation that the optimal treatment opportunity of a patient is delayed due to misdiagnosis can be avoided, optimal disease management is achieved, and the good clinical application value is achieved.