166 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 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 provides an intelligent management system for chronic diseases and common diseases of old people. The intelligent management system comprises an old people comprehensive evaluation module, a multidisciplinary collaborative diagnosis and treatment module, a multi-medication risk prediction management module, an intelligent chronic disease follow-up diagnosis management module and a health science popularization knowledge base module, the senile comprehensive assessment module inputs the personal chronic disease map of the patient into a risk factor prediction model for prediction, and outputs a comprehensive assessment result; the multidisciplinary cooperative diagnosis and treatment module is used for the first diagnosis / receiving diagnosis doctor to coordinate multidisciplinary doctors to perform cooperative diagnosis and treatment; the multi-medication risk prediction management module is used for giving comprehensive diagnosis, multi-medication management and risk prediction to the patient through collaborative diagnosis and treatment; the chronic disease intelligent follow-up diagnosis management module carries out follow-up visit tracking, health risk prediction and review prompting on the patient; and the health science knowledge base module matches and pushes corresponding health education knowledge. The accuracy and timeliness of senile chronic disease co-disease risk assessment prediction and management are improved, and a good auxiliary effect is achieved on improvement of the life quality of patients.

The invention relates to the technical field of patient information processing, in particular to an intelligent health management system for diabetic patients, which comprises the steps of acquiring continuous blood glucose monitoring data of patients, extracting blood glucose values, timestamps and patient identity information, establishing a blood glucose data set, performing data cleaning on the blood glucose data set, and removing missing values and repeated values. According to the method, after the data are cleaned and analyzed, the generated blood glucose fluctuation index can reveal the blood glucose fluctuation condition in a short time, and therefore the possible health risk is predicted. The fluctuation indexes are standardized and compared with a set threshold value, potential abnormal blood glucose events are recognized, and the response speed and preventability of disease management are improved. Meanwhile, the health risk of the patient can be quantitatively evaluated through calculation of the abnormal accumulated score, early warning is automatically triggered when the abnormal accumulated score exceeds a safety threshold value, and intervention is carried out in time. And the management efficiency and the treatment pertinence of diabetes mellitus are improved.

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 invention discloses an AI-based auxiliary system for clinical teaching of cerebrovascular diseases, particularly relates to the field of disease management, and comprises a multi-modal information summarization module, a feature extraction module, a cross-modal deduction module, a dynamic incremental learning module, a virtual case generation module and a teaching evaluation feedback generation module. According to the AI-based auxiliary system for clinical teaching of cerebrovascular diseases, millisecond-level time domain alignment is carried out through a multi-modal information gathering module, a mapping relation table of image space coordinates and physiological signal timestamps is established, the problem of time sequence deviation caused by asynchronous processing of sub-channels is solved, and time-space alignment of multi-source data is achieved; feature decoupling of images and physiological signals is achieved through a feature extraction module, and relevance between cross-modal features is enhanced; dynamic information of blood flow velocity and blood pressure fluctuation of blood vessels is fused into attention scores through a cross-modal deduction module, and a causal relationship between variables is established, so that time-space consistency of clinical logic during deduction of complex cases is improved.

The invention discloses a pine wood nematode disease intelligent identification management and control method and system based on artificial intelligence, and relates to the technical field of forest management and control, and the system discloses a pine forest region division management and control module, a pine wood nematode disease identification module, a pine wood nematode disease treatment analysis module, and a region management and control sorting module. A pine forest region division control module and a pine wood nematode disease identification module are arranged, reasonable region control is performed on a pine forest region, pine wood nematode diseases in the pine forest region are accurately identified through deep analysis of multi-dimensional disease features, and a pine wood nematode disease treatment analysis module and a region control sorting module are arranged. The pine forest simulation management and control model of the pine wood nematode disease recognition area is constructed, the actual situation of the pine wood nematode disease recognition area in the subsequent management and control process is accurately analyzed, and the current disease situation, the subsequent disease control situation and the disease influence situation of the pine wood nematode disease recognition area are quantitatively analyzed.

The invention relates to the technical field of medical health information, in particular to a patient disease evolution relation prediction method and system based on a time sequence diagram neural network, and the method comprises the steps: S01, obtaining offline diagnosis data and online diagnosis data, and building first medical record data information and second medical record data information based on the offline diagnosis data and the online diagnosis data; and S02, sorting the first medical record data information and the second medical record data information according to a time sequence, obtaining comprehensive medical record data information, analyzing all medical record data, obtaining medical record data information of a target disease, and establishing medical record data information of the target disease. According to the system and the method, offline diagnosis and online diagnosis are integrated, so that a silent period caused by a single data source can be filled, the integrity of a patient treatment record is ensured, and the continuity of disease management is improved.

The invention discloses homocysteine as a Graves eye disease biomarker and application thereof, the biomarker is homocysteine (Hcy), application of a reagent for detecting the biomarker in preparation of a product for diagnosing Graves eye diseases, and the reagent is used for detecting the level of Hcy in sample serum. By detecting the level of homocysteine (Hcy) in serum, an objective and quantitative biomarker is provided for diagnosis of Graves eye diseases (GO), the diagnosis accuracy is remarkably improved, and the defects that existing clinical activity scores are high in subjectivity and insensitive to latent patients are overcome; the Hcy level is in positive correlation with GO activity, and can be used for monitoring disease progression and treatment effect, optimizing disease management, reducing serious complications and improving life quality of patients.

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 relates to an unmanned aerial vehicle hyperspectrum-based rice bacterial blight severity inversion method, which comprises the following steps of: analyzing a spectrum curve of different types of sample point reflectivity along with wavelength change, determining sensitive wavebands of different bacterial blight harm degrees, and selecting the sensitive wavebands to construct a bacterial blight harm rice inversion index NBLBRI; and determining NBLBRI thresholds of inversion of the rice harmed by the four different bacterial blight diseases. The method has the beneficial effects that the sensitive wavebands with different bacterial blight damage degrees are determined by analyzing the spectral curve difference of the training samples, and the sensitive wavebands are selected to construct the bacterial blight damage rice inversion index NBLBRI; analyzing differences between statistical characteristics of NBLBRI between rice with different disease damage degrees and healthy rice, and determining threshold intervals of NBLBRI for estimating the damage degrees of different types of rice diseases so as to accurately extract areas with different disease degrees; and a flexible and wide application prospect is provided for agricultural disease management.

Exemplary embodiments of the present disclosure are directed towards a carrier-based agricultural biofertilizer composition for enhanced disease management in legumes includes, an isolated fluorescens strain CGA FPBPF-034 having the deposit accession number KY495220, and an agriculturally acceptable solid or liquid formulation with an organic or inorganic stabilizer to provide stability and support for the Pseudomonas species, thereby enhancing the disease management efficacy and overall health of the plants.

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 relates to the technical field of medical health, and particularly discloses a polycystic ovarian syndrome typing management system based on big data, the system integrates insulin level and LH / FSH ratio time sequence data of a patient, and adopts zero sequence processing, first order difference and time frequency analysis methods to extract metabolism and endocrine characteristics; and a comprehensive health feature vector is constructed through fusion and is input into a risk prediction model based on Bayesian network training, so that accurate identification of the type of the polycystic ovarian syndrome is realized, further, the system dynamically generates a personalized intervention scheme according to an identification result, and doctor-patient information synchronization and continuous optimization are realized through a cloud platform. By means of the method, the accuracy and scientificity of polycystic ovarian syndrome type typing are improved, individualized and intelligent disease management is achieved, and the method has good clinical application prospects and popularization value.

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.

The invention relates to a depression disease management system based on targeted metabonomics and a machine learning model, and the system is characterized in that the system comprises an intelligent diagnosis model module, a differential diagnosis model module, a grading diagnosis model module, an accompanying diagnosis model module, a treatment endpoint outcome prediction model module, a recurrence prediction model module, and a comprehensive judgment module. Through the system provided by the invention, the problems in the fields of current clinical depression diagnosis, treatment outcome, relapse and the like can be solved, accurate diagnosis and timely treatment are realized, and the mental health level of the whole people is improved.

The invention relates to the technical field of pest control, in particular to a potato disease detection system based on image recognition. The system comprises an information acquisition module, an image preprocessing module, an unmanned aerial vehicle module, a data preprocessing module, a data management and analysis module and a remote interaction module. The information acquisition module comprises an image acquisition module and a meteorological sensing module; the unmanned aerial vehicle module provides a controllable mobile bearing platform; the data preprocessing module is carried on the unmanned aerial vehicle module and is used for monitoring potato leaf diseases in real time; the data management and analysis module is used for constructing a standardized training data set to carry out model optimization training; the remote interaction module is used for completing interaction work between the unmanned aerial vehicle and the ground. According to the invention, the image information and environmental parameters in the potato growth process are obtained through unmanned aerial vehicle inspection, and the abnormal areas are subjected to key screening, so that automatic detection and classification of potato diseases are realized, and the disease management efficiency of potatoes is improved.

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.

The invention discloses an application of S100A8 / A9 in evaluating the severity of infectious diseases. Specifically disclosed is the use of a biomarker (S100A8 / A9) and / or a substance for detecting the biomarker in the preparation of a product for evaluating the severity of infectious diseases. By detecting the level of S100A8 / A9 in serum or plasma, the severity of infectious diseases is rapidly and accurately evaluated, the evaluation process is simplified, the accuracy is improved, and the kit is suitable for various infectious diseases. The biomarker is high in sensitivity and good in specificity, decision support can be provided for clinicians by evaluating the severity of the illness state, a treatment scheme can be provided for patients more accurately, the treatment effect and the living quality of the patients can be improved, meanwhile, optimal disease management is achieved, the medical burden on families and the society of the patients is relieved, and the application prospect is wide. Good and wide clinical application values are realized.

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 present invention discloses a leafy vegetable disease detection method based on few-shot learning and prototype attention, comprising the following steps: S1, preprocessing and enhancing data; S2, performing multi-scale feature extraction on images; S3, global feature modeling; S4, target detection and semantic segmentation; S5, few-shot optimization and fine-tuning. The present invention adopts the above-mentioned leafy vegetable disease detection method based on few-shot learning and prototype attention. Aiming at the problem of scarcity of agricultural disease data samples, the few-shot learning network architecture is introduced. It can realize fast and accurate disease area identification and segmentation under limited data conditions, combines target detection and semantic segmentation tasks, performs disease location and fine-grained segmentation, improves detection accuracy and segmentation performance, integrates prototype extraction with attention mechanism, effectively improves the learning ability of the model under low-sample conditions, can be applied in real time to disease detection and control in vegetable planting areas, and supports farmers in efficient disease management.

The present invention relates to a disease prediction and disease analysis method for disease management, and a device for performing the method. The disease prediction and disease analysis method for disease management may comprise the steps in which a disease management device: receives user data; and generates user disease management data on the basis of the user data.

The invention provides a brain longitudinal image generation method and device based on a novel diffusion model and a deformation field technology, a terminal, a medium and a product. The method comprises the following steps: acquiring a brain MRI image of a target detected object at a target time point and determining a time point to be predicted; obtaining a deformation field from the target time point to the to-be-predicted time point based on a trained novel diffusion model according to the preprocessed brain MRI image of the target time point and the to-be-predicted time point; and according to the brain MRI image of the target detected object at the target time point and the deformation field from the target time point to the to-be-predicted time point, based on the spatial deformation layer, obtaining a brain longitudinal image of the target detected object at the to-be-predicted time point. According to the method, the accuracy of generating the longitudinal brain image is improved, and early intervention and disease management of high-risk groups can be realized.