29 results about "Risk groups" patented technology

This invention discloses an artificial intelligence-based intervention system for tumor radiotherapy rehabilitation nursing, belonging to the field of tumor rehabilitation nursing technology. By integrating multi-dimensional data and combining clinical standard output quantitative assessment levels and key weakness dimensions, this invention accurately identifies patients' rehabilitation shortcomings, providing targeted objectives for the nursing intervention strategy generation module and generating personalized plans to ensure the intervention's relevance and feasibility. It captures strategy execution data in real time and dynamically adjusts the plan, ensuring the intervention process always adapts to changes in the patient's rehabilitation status. Simultaneously, by regularly collecting core indicators and linking risk escalation and priority intervention, it strengthens proactive management of high-risk groups, realizing a shift from passive response to proactive prediction in tumor radiotherapy rehabilitation nursing, thus optimizing the overall quality of post-radiotherapy rehabilitation nursing and patient prognosis.

The application relates to the field of biological medicine, and particularly discloses application of saliva TP17, TP47 and TP15 specific antibodies in preparation of an auxiliary diagnostic product for neurosyphilis. Based on specific antibody immunoglobulin G (IgG) of Treponema pallidum in saliva, combined detection of saliva TP17 / IgG, TP47 / IgG and TP15 / IgG outputs a risk value of neurosyphilis of a subject, or a four combined Logistic regression discriminant model is constructed by combining serum TRUST titers, and the subject is stratified into a low-risk or high-risk group based on a preset judgment threshold, so that the comprehensive recognition capability for neurosyphilis is improved. The saliva is used as a detection sample, and the application has the advantages of non-invasiveness, convenience, easy popularization, repeatable collection and the like, and can be used for early screening, auxiliary diagnosis and clinical path management of neurosyphilis.

The application discloses a kind of chronic coronary syndrome risk stratification methods based on coronary microcirculation resistance index, it is related to medical data processing technical field.The method comprises: the clinical characteristic data of patient to be evaluated is obtained;The clinical characteristic data is input into the preset risk stratification model, and the risk group of the patient to be evaluated is obtained according to preset split condition.The preset split condition determines the split priority by analyzing the nonlinear correlation and the degree of distinction of multidimensional feature data, and generates a multi-layer classification model by combining survival tree algorithm training.The application breaks through the limitation of single index evaluation, effectively captures the nonlinear interaction between clinical indicators, significantly improves the risk stratification accuracy, can accurately identify the very low risk and high-risk subgroup, avoid clinical over-treatment or insufficient intervention treatment, and the model has strong interpretability and is easy to promote clinically.

The present application belongs to the technical field of medical artificial intelligence auxiliary decision-making, and particularly relates to a method for predicting postoperative delirium risk of elderly abdominal surgery patients based on perioperative clinical characteristics. The present application extracts the demographic characteristics, preoperative laboratory indicators and intraoperative anesthesia-related characteristics of elderly abdominal surgery patients based on an electronic medical record system, and constructs a full-feature data set for postoperative delirium risk prediction. Further, a machine learning algorithm is used to establish a postoperative delirium risk prediction model, and the target model is screened in combination with the model stability evaluation result. On this basis, the model is optimized by using SHAP feature contribution analysis and recursive feature elimination strategy, and a simplified prediction model with fewer variables and better generalization performance is obtained. The prediction model can realize early identification of high-risk groups of postoperative delirium of elderly abdominal surgery patients during the perioperative period, has high discrimination ability, stability and clinical applicability, and can provide a reliable tool for precise prevention and auxiliary decision-making during the perioperative period.

This invention discloses a risk prediction device and storage medium for metabolic-associated fatty liver disease in children or adolescents. The two-stage cascaded screening strategy and model of this invention provide a highly accurate, accessible, and scalable solution for the early identification of metabolic-associated fatty liver disease in children or adolescents. This invention utilizes different anthropometric data to identify high-risk groups in routine school physical examinations, followed by further screening using a comprehensive serological index at community health centers, thereby achieving accurate diagnosis of metabolic-associated fatty liver disease and providing clinicians with a non-invasive auxiliary diagnostic tool that combines high precision, high accuracy, and broad accessibility.

This invention discloses a method for constructing a gene methylation prognostic assessment model for differentiated thyroid cancer, characterized by the following steps: S1, obtaining a test sample; S2, extracting and storing DNA from the test sample; S3, performing methylation analysis; S4, constructing a prognostic classification model based on DNA methylation, calculating the risk value of the prognostic model as Risk Score = 0.15411928*cg03190661 - 0.10405129*cg15676916 + 0.06108015; S5, cross-validating to evaluate performance and obtain the gene methylation prognostic assessment model. This model uses the Risk Score to assess the prognosis of differentiated thyroid cancer: low-risk group, normal follow-up is recommended; medium-risk group, follow-up time can be reduced to half; high-risk group, close follow-up is required.

This invention provides a method for treating postoperative nausea and vomiting using buccal acupuncture combined with a triple antiemetic regimen. The method employs a combined intervention of buccal acupuncture and a triple antiemetic regimen. The triple regimen consists of a 5-HT3 receptor antagonist, a glucocorticoid, and a dopamine receptor antagonist, administered intravenously in two separate doses. The buccal acupuncture uses CA-2 (upper jiao), CA-3 (middle jiao), and CA-4 (lower jiao) as core acupoints, with auxiliary and reinforcing acupoints added based on the surgical site and high-risk groups. A standardized procedure of pecking stimulation is applied, along with a corresponding postoperative remedial antiemetic medication regimen. This invention organically combines multi-target drug blockade with buccal acupuncture's visceral regulation, synergistically enhancing the antiemetic effect, reducing the incidence of nausea and vomiting, and decreasing the need for remedial medication. The regimen is highly standardized, widely adaptable, and can meet the postoperative nausea and vomiting control needs of various surgeries and high-risk groups. It exhibits good safety, strong repeatability, and high clinical application value.

The application discloses a virus marker for depression diagnosis and a screening method and application thereof, relates to the technical field of biological detection, and the virus marker for depression diagnosis comprises at least one of s_Stenotrophomonas_virus_Pokken, g_Pokkenvirus, s_Dickeya_virus_AD1 and g_Alexandravirus. The application firstly takes the enterovirus group characteristics as the biomarker of depression, breaks through the limitation that traditional mental illness diagnosis relies on subjective scale evaluation; compared with the prior art, the four virus markers can be used for the diagnosis of clinical depression alone or in combination, have the characteristics of non-invasiveness and high sensitivity, can effectively distinguish the depression patients from the healthy people, can early discover the high-risk groups, and provide a new objective diagnosis method for the precise typing of depression.

Disclosures herein are directed to methods and compositions for predicting high- and low-risk for liver fibrosis progression in patients. Based on the results achieved from the methods and compositions disclosed herein, liver disease patients can be classified into a prognostic risk group, which enables early diagnosis and prevention of liver fibrosis and other lethal complications. Methods and compositions disclosed herein substantially improve the poor prognosis of subjects having or at risk for one or more liver fibrosis.

The application discloses a molecular marker and MRI image fusion tumor early detection method and system, first collects serum molecular marker detection data and multi-sequence MRI image data of a digestive tract tumor screening object, constructs a molecular feature vector and extracts high-dimensional MRI image features respectively; a tumor prior probability is calculated through a molecular pre-screening deep neural network, and an adaptive spatial attention guide weight is generated; the weight is weighted and fused with the MRI image features to realize fine feature enhancement of a high-risk area and accurate lesion detection; based on a Bayesian evidence fusion reasoning framework, molecular detection evidence and image detection evidence are combined to update a posterior probability, and finally, a lesion positioning, detection confidence and visual detection report are output. The application realizes efficient complementation of molecular evidence and image evidence, greatly improves the accuracy, sensitivity and specificity of early detection of digestive tract tumors, and is suitable for non-invasive accurate screening of high-risk groups in clinical practice.

This invention discloses an intelligent self-rescue system for marine jellyfish stings, relating to the field of first aid technology for biological injuries. This invention establishes a basic concept popular science system by integrating information on the species, habits, geographical distribution, and hazards of marine organisms to prevent marine organism injuries. It also integrates the clinical manifestations and treatment plans for injuries caused by venomous marine organisms, establishing a training system for treatment skills to aid in the preliminary identification of injury types and improve treatment skills. Furthermore, it integrates relevant data to compile a first aid guidebook and presents relevant medicines and equipment in a modular format for on-site emergency treatment. This effectively addresses the shortcomings and deficiencies in current marine organism injury treatment. Based on the epidemiological characteristics and treatment consensus of marine jellyfish stings, it improves the awareness, treatment skills, and on-site emergency treatment efficiency of potentially high-risk groups and medical personnel regarding marine jellyfish stings, reducing the risk of aggravated injury and mortality from jellyfish stings.

The application provides a construction method and system of a model for predicting ovarian low response and deploying individualized ovarian stimulation strategies, including two models: a POR diagnosis model and a POR ovarian stimulation strategy deployment model. First, candidate features are preliminarily screened and data is preprocessed; second, the model is trained based on various machine learning algorithms, the best algorithm is evaluated and screened; third, the key risk factors related to POR are determined through feature interpretation; and finally, the model construction, testing and verification are completed, and the prediction is interpretable at the individual level. The application can make interpretable prediction on the occurrence risk of POR of women of childbearing age, and can deploy individualized optimal ovarian stimulation strategies based on the POR risk, can be applied in a hospital information system scene, has important research significance for the clinical diagnosis and pathogenesis of POR, and has great use value for early screening of high-risk groups of POR, assisting doctors in making diagnosis and treatment decisions and reducing the economic burden of patients.

The application relates to the field of molecular biomedical technology, and discloses a method, system and device for evaluating recurrence risk and prognosis of a resectable non-small cell lung cancer (NSCLC) patient based on plasma circulating free DNA (cfDNA) fragmentomics. The method comprises the following steps: obtaining plasma cfDNA of the NSCLC patient and performing high-throughput sequencing; extracting fragment end mode characteristic values (CsFMA) of GATA3 and CEP57 genes from the sequencing data, which are CsFMA.GATA3 and CsFMA.CEP57 respectively; inputting the characteristic values into a risk score model; calculating an individual risk value; stratifying the patient into a high-risk group and a low-risk group according to the risk value, so as to predict disease-free survival and overall survival. The application discloses a strong correlation between specific fragmentomic characteristics of GATA3 and CEP57 genes and recurrence and prognosis of NSCLC, and the constructed model can effectively predict recurrence risk and prognosis. The application provides a non-invasive and accurate new tool for resectable NSCLC recurrence and prognosis stratification and individualized treatment decision-making.

The application discloses a Parkinson disease dysarthria early identification method based on voiceprint features and belongs to the technical field of medical diagnosis. The original speech data of a to-be-identified object is subjected to speech analysis and speech recognition, and an initial feature set related to voiceprints in a pronunciation deviation and a segment is extracted, target features with significant discriminability for Parkinson disease dysarthria identification are screened out, a dysarthria identification result of the to-be-identified object is obtained through a model, accurate vowel and consonant segment extraction and pronunciation deviation identification are realized, phoneme segment accurate cutting is realized by combining the association of a text sequence and speech data, the quantization determination of the pronunciation deviation is realized, the key dimension of the dysarthria identification is reserved through feature extraction, the early signals of Parkinson disease dysarthria are captured, the high-risk groups are marked, the early warning result is output in combination with the joint risk value calculation, the problem that the prodromal symptoms are not obvious is solved, the review interval is dynamically adjusted, and the whole-process management from identification to monitoring is realized.

This invention belongs to the field of hospital fungal infection detection technology, and relates to a method and system for predicting the risk of fungal infection in hospital settings. The method includes: when a sub-area within a ward is identified as a warning area based on a comprehensive analysis of the patient's vital signs and the historical operating records of diagnostic equipment, the invention first obtains the patient's vital signs to assess their probability of fungal infection and generates a multi-level risk classification accordingly; the patient's risk group and sub-area information are input into the analysis model to determine whether the sub-area is a normal area or a warning area. By combining the risk assessment of the patient with the risk determination of the sub-area, the warning area is identified. Compared to existing technologies that only focus on the patient, this invention can locate sub-areas with concentrated infection risk, allowing prevention and control resources to be allocated to the warning area, thereby improving the targeting and efficiency of prevention and control.

The application relates to the technical field of power distribution network equipment fault risk prediction, and discloses a power distribution network equipment fault risk prediction method based on meteorological factors, equipment and a medium. The method collects operation state data and meteorological data of target equipment, extracts a feature vector, and divides the feature vector into a historical risk group and a real-time risk group. A structure relationship model is constructed based on the risk groups, and an initial path weight matrix is calculated. A comprehensive meteorological mutation index is calculated, and it is judged whether the initial path weight matrix needs to be adjusted. If yes, a potential function model is constructed, an extreme value response of the potential function is solved as an adjustment factor, the initial path weight matrix is corrected, and a new path weight matrix is obtained. The real-time feature vector of the target equipment is input into a fault prediction model, a fault occurrence probability is obtained, and the total equipment fault risk is calculated in combination with the new path weight matrix. The application can adaptively optimize prediction based on real-time meteorological changes, and the real-time performance and robustness of the optimized prediction are improved.

The invention relates to the technical field of big data and artificial intelligence, and discloses a rural traffic accident high-risk crowd identification method based on signaling data fusion and a space-time diagram convolutional network, and the method comprises the steps: taking mobile signaling data as a core, fusing multi-source data, carrying out privacy protection preprocessing, extracting the space-time features of rural users through dynamic diagram modeling, and carrying out the reconstruction of the space-time features of the rural users; space-time association is captured by means of a space-time diagram convolutional network fusing an attention mechanism, a rural scene exclusive rule is accurately captured through dynamic graph modeling and regional risk factor injection, and cross-domain privacy security training is realized in combination with federated learning and homomorphic encryption. And finally, through integration of a space-time diagram convolutional network and a federated safety tree model, risk grade division is assisted by a gray system, and risk probability distribution of rural traffic accident high-risk crowds is accurately output, so that a clear object is provided for targeted propaganda and education, police waste caused by indifference propaganda and education is reduced, the rural traffic accident prevention and control efficiency is effectively improved, and the accident rate is reduced.

This invention relates to the field of smart elderly care monitoring technology, and discloses a monitoring method and system for high-risk groups in elderly care institutions based on integrated medical and elderly care. By modeling the process of returning to the room after an activity as a complete care task, walking, pausing, turning, and stopping at the door in video images are no longer understood as ordinary actions in isolation, but are continuously analyzed under the constraint of whether the elderly person successfully returns to their own room. Features such as target consistency, head and body orientation differences, approaching the elderly person's own room door without entering, and stopping at the door of a non-elderly person's room are introduced. Before the elderly person falls or mistakenly enters another room, the system can identify whether the process of returning to the room has deviated. It can also restore the disorientation behavior that is fragmented under the condition of crowd obstruction to the same continuous event, and more accurately distinguish between normal return to the room, short-term waiting, and true disorientation and wandering. This solves the problem that existing monitoring methods are difficult to continuously identify the deviation of the intention of high-risk elderly people returning to their rooms and the disorientation and wandering process in the scenario of returning to the room after an activity.

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.

This invention relates to the field of molecular detection technology, specifically to a DNA tetrahedral probe for detecting lung cancer gene methylation. The probe is formed by the self-assembly of four single-stranded DNA strands S1-S4, with S1 extending into a recognition arm targeting the methylated fragment of the PTGER4 promoter region. This invention immobilizes the tetrahedral probe on the inner wall of a nuclear pore membrane, preparing it as the core electrochemical detection component of a reagent kit. Utilizing the specific recognition capabilities of the nuclear pore membrane and the probe, combined with electrochemical detection technology, rapid detection of PTGER4 gene methylation is achieved without the need for PCR nucleic acid amplification. This detection system is simple to operate, low in cost, and can be widely applied to screening high-risk groups for lung cancer.

The invention relates to the technical field of image analysis, in particular to a multi-modal fusion glioma intelligent prognosis prediction method and system, and the method comprises the steps: collecting multi-modal data, respectively extracting omics features, constructing a multi-modal fusion depth model, obtaining high and low risk groups, and respectively carrying out difference analysis from multiple omics dimensions, screening out a gene set which is remarkably and highly expressed in the high-risk group, obtaining an intersection of each group of study difference analysis results, preliminarily screening out candidate genes, carrying out inter-study correlation analysis, and screening out intersection genes; introducing the intersection genes into a public database, and screening out genes which are remarkably and highly expressed in GBM to obtain candidate genes; and respectively introducing the candidate genes into a plurality of public databases, and finally identifying potential treatment targets related to the high-risk group. According to the method, the accuracy, the interpretability and the clinical transformation potential of GBM patient layering are comprehensively improved by constructing the multi-modal fusion depth model and combining heterogeneity mechanism analysis and target screening verification.

This invention relates to a biomarker for diagnosing rheumatoid arthritis (RA) and its application, belonging to the field of biomedical technology. This invention discovered that the concentrations of lipopolysaccharide (LPS) and diamine oxidase (DAO) in the plasma of RA patients are significantly higher than those in healthy controls. Furthermore, this invention also found that three proteins derived from plasma exosomes—phospholipid invertase 1 (PLSCR1), keratin 1 (KRT1), and complement C1QC chain (C1QC)—have good diagnostic effects on RA. Combining LPS and DAO with these three proteins can significantly improve the diagnostic accuracy of RA. Therefore, applying the biomarker of this invention to the preparation of RA diagnostic products can provide a core basis for the accurate diagnosis of RA and the generation of risk assessment systems, and has translational application value in the early diagnosis of RA and the screening and precise prevention and treatment of high-risk groups.

This invention relates to the field of data analysis technology and discloses a method, apparatus, device, and medium for identifying users associated with abnormal feature addresses. The method includes: acquiring the identification information of controlled carriers involved in known abnormal usage events; extracting the corresponding location device installation address and activation address; identifying duplicate addresses and marking them as abnormal feature addresses; and using the abnormal feature addresses to reverse-search other controlled carriers installed or activated at those addresses. After excluding controlled carriers involved in known abnormal usage events, the method obtains the associated user identity information and generates a list of potential abnormal users. This invention can be applied to business scenarios such as fintech, establishing the association between addresses, carriers, and users through the clustering characteristics of location device addresses, enabling automatic identification of potential risk subjects under the same abnormal environment, and improving the efficiency and coverage of risk group investigation.

The application discloses an e-commerce order data management method based on intelligent analysis, and relates to the technical field of e-commerce data processing.The method comprises the following steps: marking a high-risk group through closed-loop detection according to the correlation strength in a feature topology network; analyzing the geographical position distribution characteristics and behavior mode characteristics of the high-risk group to obtain risk assessment parameters; performing real-time transaction interception on e-commerce orders marked as the high-risk group based on the risk assessment parameters; generating a resource allocation scheme according to the geographical position distribution characteristics and behavior mode characteristics of the high-risk group; extracting attack features in the resource allocation scheme to dynamically reconstruct the feature topology network; updating the correlation strength calculation logic in the topology relationship model based on the reconstructed feature topology network, synchronously fusing the geographical position distribution characteristics in the resource allocation scheme, and optimizing the density gradient analysis algorithm.The application converts resource allocation into an algorithm optimization signal, thereby improving the complex attack mode recognition rate and resource allocation efficiency.

The application discloses a kind of risk stratification model construction method and system for EGFR mutant non-small cell lung cancer meningeal metastasis patient, belong to tumor prognosis evaluation technical field.The multicenter retrospective clinical database is first constructed and data preprocessing is completed, then the predictor is screened by LASSO Cox regression, and the five core prognostic factors of ECOG PS score, brain metastasis, MRI meningeal enhancement, CSFC result and intracranial pressure are determined by multi-factor Cox proportional risk regression and linear predictor is constructed, then it is converted into integer type weighted risk score, finally the best cutoff value is determined based on survival function, and the patient is divided into low-risk group and high-risk group.The application can clearly distinguish the patient group with significant difference in prognosis, the required indicators are all clinical routine items, the operation is simple, the accessibility is high, can accurately guide individualized treatment decision and clinical trial stratification, and has important clinical application value.

The application discloses a method for differentiating Late-Life Depression (LLD) and amnestic Mild Cognitive Impairment (aMCI) based on plasma metabolomics combined with neural cognitive evaluation. The application detects metabolite spectrum in the plasma of the subject by hydrogen proton magnetic resonance spectrum metabolomics technology, screens out Scyllo-inositol as a key differential marker, and combines with neural cognitive test scores (especially memory scale scores) to construct a joint diagnosis model. The method can effectively distinguish LLD, aMCI and healthy control groups, and has high diagnostic accuracy and specificity. In addition, the method further introduces two-way double-sample Mendelian Randomization (MR) analysis to verify the causal relationship between Scyllo-inositol and the risk of dementia from the genetic point of view, and to strengthen the reliability of Scyllo-inositol as a biomarker. The application provides a diagnosis method with both metabolomics and genetic evidence support for early identification, accurate typing and intervention of high-risk groups of senile dementia, and has important clinical application value.

A prognostic risk analysis system for head and neck cancer includes a data collection module, a data processing module, a model training module, an analysis module, a risk stratification module and a clinical application module. The data collection module collects a training data of patients, each training data including a clinical data and an image data. The data processing module performs a preprocessing process on the training data to extract first feature values. The model training module trains a machine learning algorithm to build a prediction model by using the first feature values. The analysis module analyzes, based on the prediction model, a dataset of a patient to generate a prognostic risk data. The risk stratification module stratifies the prognostic risk data into a low-risk group, an intermediate-risk group or a high-risk group. The clinical application module applies the prognostic risk data and the risk groups to clinical practice.

Embodiments of the present application provide an insurance claim fraud identification method and device, equipment and storage medium, belonging to the field of artificial intelligence. The method comprises: obtaining historical abnormal medical behavior data and behavior correlation data of a target object; constructing a relationship graph of the target object according to the historical abnormal medical behavior data and the behavior correlation data, to obtain an abnormal relationship graph; dividing the target object into groups according to the abnormal relationship graph, to obtain a candidate group; wherein the candidate group includes a first risk group with a first risk category; classifying the target objects of the first risk group according to node attribute characteristics, to obtain object risk categories; and identifying the target object for fraud according to the object risk categories and the first risk group. The present application can be applied to financial technology and other business systems that require a large amount of data, can save the cost of human resources for fraud identification, and improve the accuracy of fraud identification.

This invention discloses a biomarker combination and its screening method, relating to the field of biotechnology. The biomarker combination includes lactate, glutamine, arginine, and 3-methylpentenoic acid, applied to assess the rupture risk of cerebral aneurysms. The screening method for the biomarker combination involves: collecting metabolic fingerprints from serum of patients with cerebral aneurysms and healthy controls using nanoparticle-enhanced laser desorption / ionization mass spectrometry; and performing machine learning to screen the biomarker combination. This invention utilizes nanoparticle-enhanced laser desorption / ionization mass spectrometry to screen lactate, glutamine, arginine, and 3-methylpentenoic acid as metabolic biomarkers, and uses these biomarkers to assess the rupture risk of cerebral aneurysms. Significant differences were observed between the low-risk and high-risk groups (P < 0.05).