13 results about "Disease progress" patented technology

The invention discloses an intelligent health early warning system based on deep learning, and relates to the technical field of medical health, and the system employs a causal convolutional network and a gating circulation unit double branch to extract long and short term features, generates a dynamic threshold value through adaptive weight fusion in combination with a dynamically updated personalized health baseline and a disease knowledge graph, and carries out the early warning of the health. The method has the advantages that long-scale health trend features are mined through the long-period feature extraction module, short-scale sudden abnormal features are captured through the short-period feature extraction module, and two types of feature weights are dynamically distributed and fused based on a medical risk level judgment rule in combination with the self-adaptive weight fusion module; and a personalized dynamic early warning threshold adaptive to the health state of the user is generated by combining a dynamic threshold generation module with a disease progress pathology knowledge graph, so that the defects that an existing system cannot separate long and short-term health signals and depends on a fixed threshold, so that an early hidden risk signal is covered, early warning is lagged and the false alarm and missing report rate is high are effectively overcome.

The invention discloses a Crohn disease postoperative bad outcome prediction method combining knowledge base construction and an experience-driven self-asking mechanism, and solves the problems of insufficient interpretability, insufficient clinical data utilization, weak generalization ability and the like of an existing prediction method. The method comprises the following steps: constructing a Crohn disease special medical knowledge base, extracting disease progress related factors from medical literatures and real medical records, and carrying out expert score weighting processing to form searchable knowledge entries; retrieving related knowledge injection context from a knowledge base based on a medical record input by a user, and generating preliminary prediction by using a large language model; structured reasoning is guided through a multi-layer prompt engine, diagnosis tasks are decomposed through the thinking chain technology, and self-check before reasoning is achieved by driving a self-asking mechanism through experience. And generating an introspection problem chain according to historical error cases, iteratively optimizing the reasoning process, and finally outputting a bad outcome prediction result and a detailed analysis report. According to the method, both traceable interpretability and prediction accuracy are considered, and reliable assistance is provided for postoperative clinical management of Crohn's disease.

The embodiment of the invention discloses a Crohn disease longitudinal queue course prediction system based on time perception Transform, and relates to the technical field of medical data processing, the system comprises a data preprocessing module, an input embedding layer, a Transform encoder and a multi-task multi-time window prediction header, the system breaks through the limitation that only baseline data is used for prediction of a traditional model, and the prediction efficiency is improved. According to the method, the full-cycle longitudinal data (such as inflammation indexes and medication records of each treatment) of the patient from the morbidity to the prediction can be integrated, and the disease progress, operation and medication risks of 1 year / 3 years / 5 years can be dynamically output. And reference is provided for clinicians.

The invention relates to the technical field of plant disease control, and particularly discloses a screening method of a cruciferous crop clubroot control agent, which comprises the following steps: after accelerating germination of cruciferous crop seeds, transplanting the cruciferous crop seeds into a 1 / 2 MS solution, and culturing the cruciferous crop seeds to a two-leaf one-core stage to obtain seedlings; soaking the roots of the seedlings in a mixed system containing plasmodiophora brassicae spores and a medicament to be detected, and inoculating; and transplanting the inoculated plants into a newly prepared 1 / 2 MS solution for continuous culture, and regularly investigating morbidity and prevention and control effects to screen out the medicament with prevention and control effects on the clubroot of the cruciferae crops. According to the method provided by the invention, continuous and non-destructive dynamic observation on the same plant can be allowed, the disease progress is accurately monitored, and the medicament screening cost is greatly reduced.

The utility model relates to an auxiliary observation device for an abdominal cavity drainage bag, relates to the technical field of medical equipment, and is used for solving the problem that in the prior art, the color and the amount of liquid in the drainage bag cannot be clearly and accurately checked in a dark environment, so that the disease progress and the treatment effect of a patient cannot be accurately judged. Comprising a transparent placing box, an induction lighting piece, a weighing piece and a mounting frame used for being fixedly mounted on a wall, the weighing piece is fixedly mounted on the mounting frame, and the transparent placing box is detachably hung on the weighing piece; the transparent placing box is used for placing a drainage bag, the induction lighting part used for lighting is arranged below the transparent placing box, a light beam of the induction lighting part is arranged upwards, and a limiting part used for fixing the drainage bag is arranged on the transparent placing box.

The invention relates to an interpretable Alzheimer's disease deep learning prediction method and system. The method comprises the following steps: acquiring multi-modal data of a candidate AD patient, wherein the multi-modal data comprises sMRI images and clinical and lifestyle data; processing the multi-modal data by using a pre-trained multi-modal deep fusion prediction model to obtain an AD risk level and a disease progress probability; wherein multi-modal features are extracted through the sub-modal feature extraction layer, deep fusion is carried out on the multi-modal features through the fusion modeling layer to obtain a global fusion feature vector, the global fusion feature vector is processed through the result output layer, and a risk level classification result and a disease progress probability in future preset time are obtained. By adopting the method, multi-modal heterogeneous data can be effectively fused, qualitative risk grading and quantitative disease course prediction are provided, and the diagnosis comprehensiveness is improved.

The invention discloses an apple leaf ring spot early diagnosis and chlorophyll content prediction method based on hyperspectral imaging and a CNN-LSTM mixed model. According to the method, hyperspectral images of apple leaves within the range of 400-1000 nm are collected through a hyperspectral imaging system, characteristic wave bands related to the disease progress and chlorophyll change are screened through a competitive adaptive reweighted sampling (CARS) and a continuous projection algorithm (SPA), and a convolutional neural network and long-short term memory network (CNN-LSTM) mixed model is input; early recognition of diseases and synchronous prediction of chlorophyll content are realized. And the chlorophyll prediction model is used for pixel-by-pixel prediction of the leaf spectral image, so that a spatial distribution visual image of the chlorophyll content of the apple leaf is realized, and lossless, rapid and quantitative evaluation of the health state of the apple leaf is realized.

The invention discloses a microbial agent based on the synergistic effect of bifidobacterium pseudolongum and bifidobacterium animalis and application, and belongs to the technical field of biological medicine. The active ingredient of the microbial agent is a viable bacterium mixture of bifidobacterium pseudolongum and bifidobacterium animalis, or a metabolite co-cultured by the bifidobacterium pseudolongum and the bifidobacterium animalis. According to the microbial agent provided by the invention, an intestinal flora-butyric acid-peripheral / central immune axis is adjusted through a metabolic synergistic effect of the two strains, A beta deposition and neuroinflammation can be intervened from a pathologic source so as to intervene the progress of the Alzheimer's disease, and the safety is good. The synergistic effect of the bifidobacterium pseudolongum and the bifidobacterium animalis is applied to regulation of intestinal flora and intervention or auxiliary improvement of memory of the Alzheimer's disease, and a novel biological preparation and a technical path are provided for prevention and treatment of neurodegenerative diseases such as the Alzheimer's disease.

The invention discloses a method for evaluating sepsis lung injury by detecting the crosslinking degree of type VI collagen, and relates to the technical field of medical diagnosis and monitoring. The method at least comprises the following steps: S1, firstly, carrying out sample collection and multi-index detection; s2, performing imaging feature extraction, and automatically identifying fine structure changes in the chest CT image by using a deep learning algorithm; and S3, based on the feature extraction in the S2, establishing a corresponding quality control algorithm, a basic damage scoring model, a dynamic progress prediction model and a comprehensive risk assessment model. The accurate, early-stage and dynamic sepsis lung injury assessment method is provided through multi-dimensional biomarker detection, iconography feature extraction and a dynamic algorithm model, the early diagnosis rate of sepsis lung injury can be remarkably improved, the disease progress can be effectively monitored, the treatment effect can be effectively assessed, and the application prospect is wide. The method has remarkable clinical application prospects and economic benefits, and particularly has important value in intensive care and formulation of personalized treatment schemes.

This application discloses an intelligent medical management system for patients with bone injuries, including the following modules: a scanning module for acquiring imaging images of the affected area of ​​bone injury patients, including CT images, X-ray images, and ultrasound images; adjustable scanning parameters to adapt to the scanning needs of bone injuries in different locations and at different stages of the disease; and an image analysis module that automatically performs denoising, enhancement, and precise segmentation processing on the original scan images to achieve accurate quantitative assessment of the bone injury condition, generate standardized processed images and feature parameter reports, effectively reduce the workload of medical staff, improve diagnostic and treatment efficiency, and provide objective data support for clinical diagnosis and treatment. The prediction module calls historical case data and current patient disease progress data from the data storage module to identify high-risk groups for complications in advance and output targeted intervention suggestions, effectively avoiding poor patient rehabilitation, secondary injury, and aggravation of complications, thereby improving the quality of patient rehabilitation.

The invention relates to the technical field of medical care, and particularly discloses a core tube of a nursing strategy generation method for a hepatocellular carcinoma interventional post-operation, which comprises the following steps: systematically acquiring clinical parameters of a patient at multiple time points after a TACE operation and a patient report outcome, including symptom expression, complication and life quality data; performing multi-dimensional analysis on the symptom trajectory, the complication severity and the disease progress risk based on a preset rule, and dynamically evaluating the recovery risk level of the patient; and then, according to the risk level and the specific stage of the post-operation, a corresponding structured nursing strategy template is intelligently matched from a knowledge base constructed based on evidence, personalized fine adjustment is carried out on the template in combination with the individual condition of the patient, and a comprehensive nursing document containing symptom management, nutrition, activity, psychology and social support is generated and safely output to a medical system. By implementing closed-loop management of evaluation, strategy generation and dynamic adjustment, the accuracy, systematicness and individuation level of postoperative care are effectively improved.

The application discloses application of UFC1 as a biomarker in preparation of reagents for diagnosing and / or treating prostatic hyperplasia. In the technical scheme, the cell experiment result shows that UFC1 can significantly inhibit the proliferation ability of WPMY-1 and BPH-1 cells, and can effectively promote the apoptosis of the cells. The finding reveals the core driving role of UFC1 in BPH progress, that is, maintaining the imbalance between the proliferation and apoptosis of prostate cells, and promoting the tissue hyperplasia. The mechanism indicates the precise intervention direction of the targeted treatment: if the activity or expression of UFC1 can be specifically inhibited, the abnormal proliferation of prostate cells can be blocked from the source, the normal apoptosis program of the cells can be restored, and the proliferation progress can be reversed by realizing the "two-way regulation". Compared with the existing alpha receptor blockers which can only relieve the obstruction symptoms, and the 5 alpha-reductase inhibitors which need long-term medication and can cause sexual dysfunction, the treatment strategy of targeting UFC1 directly attacks the biological nature of the hyperplastic cells, and is expected to more effectively control the disease progress.