125 results about "Risk groups" patented technology

The invention discloses a sudden death risk real-time assessment system and method based on multi-modal physiological signal fusion, relates to the field of human physiological state monitoring and early warning, and solves the problems of low accuracy and poor real-time performance of sudden death risk assessment by single-modal signals. The system comprises a signal acquisition module, a preprocessing module, a high-dimensional feature extraction module, a multi-modal feature fusion module, a sudden death risk quantification module, a model updating engine module and an early warning feedback module, and each module integrates a self-adaptive filtering unit, a time sequence convolutional network unit, an improved multi-head self-attention mechanism unit and the like. According to the scheme, multi-mode signals such as electrocardio are synchronously collected through multiple channels, a dynamic risk index is calculated through self-adaptive noise reduction, parallel extraction of time-frequency domain nonlinear features and mutual information weighted fusion in combination with kernel density estimation, and online incremental updating and multi-stage early warning of a model are achieved; the sudden death risk can be accurately evaluated in real time, the anomaly detection sensitivity and the early warning timeliness are improved, and the method is suitable for daily health monitoring and high-risk group risk management and control.

The invention relates to the technical field of medical informatization, in particular to an MCI cognitive trajectory and bidirectional outcome dynamic prediction method based on a joint model. The method comprises the following specific steps: firstly, constructing a univariate joint model, then constructing a multivariate joint model, and then constructing a function type multivariate joint model; performing parameter estimation and statistical inference on the model; fitting evaluation is carried out on the function type multivariable joint model based on multi-longitudinal cognition and multi-state outcome; and finally, carrying out individualized dynamic prediction. According to the method, a baseline or longitudinal nerve image is processed by using a function type data analysis technology, and a change track is reconstructed; a plurality of cognitive assessments are used as longitudinal measurement indexes, covariants such as age and gender and functional nerve images are used as predictive factors, two-way outcome of MCI and occurrence time of the two-way outcome are set as survival outcomes, a joint model based on multi-longitudinal cognitive multi-state outcomes is constructed, early screening of high-risk groups is assisted, and the survival outcomes of the high-risk groups are determined. And powerful support is provided for optimal distribution of medical and medical and health resources.

A first risk score for each member in a population is generated using a first predictive model. A second risk score is generated for each member in the population using a second predictive model. The population is stratified into a plurality of risk groups based on: (i) predefined manageable medical conditions; (ii) the first risk score; and (iii) the second risk score. Based on the plurality of risk groups, the population is segmented into a high-risk group and a low-risk group. A panel of care management associates of a plurality of care management associates is assigned to each member in the member population based on whether the member belongs to the high-risk group or the low-risk group.

The invention discloses a multi-modal feature integrated risk assessment method and system for a stroke dangerous group, and relates to the technical field of telemedicine collaboration, a telemedicine collaboration network platform is built, and multi-modal data of a stroke high-risk group is collected and processed; analyzing the relation between risk factors and a cerebral apoplexy pathological mechanism by applying bioinformatics and medical knowledge, and defining a key action path; and based on an analysis result of the key action path, integrating multi-modal data by taking a pathological mechanism as an axis, and constructing a dynamic risk knowledge network based on a knowledge graph. According to the method, multi-modal data are integrated, a comprehensive patient individual feature matrix is constructed, a dynamic risk knowledge network is combined, and the association between patient individual features and a cerebral apoplexy pathological mechanism and the dynamic change of risk factors are accurately analyzed, so that an accurate risk score is calculated, the cerebral apoplexy risk of a cerebral apoplexy risk crowd is analyzed, and the cerebral apoplexy risk of the cerebral apoplexy risk crowd is analyzed. A powerful basis is provided for prevention of the cerebral apoplexy, and the occurrence rate of the cerebral apoplexy is reduced.

The invention relates to the technical field of biomedical engineering, and discloses a method and system for early warning and analyzing high-risk groups with high altitude polycythemia, and the method comprises the steps: screening a subject data set from an extremely high altitude area; performing structured processing on the subject data set to obtain a structured feature matrix, and extracting a core prediction factor from the structured feature matrix; candidate early warning models of the core predictive factors are generated, and the candidate early warning models comprise a logistic regression model, an XGBoost model and a random forest model; screening an optimal early-warning model from the candidate early-warning models, and establishing a high altitude polycythemia early-warning system of the subject data set through the optimal early-warning model; and effect verification is carried out on the high altitude polycythemia early warning system so as to realize high altitude polycythemia high-risk group early warning analysis processing of the subject data set. According to the method, the core problem that the early warning result is one-sided and unreliable due to three defects of data dimension missing, static evaluation limitation and extensive privacy mechanism can be solved.

The invention discloses a data mining technology-based construction method and application of an elderly diabetic hypoglycemia risk prediction model, and belongs to the technical field of medicines and computers. The method comprises the following steps: selecting elderly diabetic patients meeting standards, collecting demographic, disease and lifestyle factor data, dividing a training set and a verification set, comparing the performance of five data mining models, screening an optimal model, confirming the effectiveness of the model through external verification, and visualizing the result. The model can output a quantitative risk probability, is suitable for multi-scene real-time evaluation, can accurately identify high-risk groups, guides hierarchical intervention, reduces the incidence rate of hypoglycemia, and improves the management efficiency and safety of elderly diabetic patients.

The invention discloses a tumor radiotherapy rehabilitation nursing intervention system based on artificial intelligence, and relates to the technical field of tumor rehabilitation nursing. By integrating multi-dimensional data and combining clinical standards to output quantitative evaluation levels and key weak dimensions, rehabilitation weakness of a patient is accurately positioned, a targeted target is provided for the nursing intervention strategy generation module, a personalized scheme is generated, and pertinence and performability of intervention are ensured; policy execution data are captured in real time and a scheme is dynamically adjusted, so that the intervention process always adapts to the rehabilitation state change of a patient, and meanwhile, active management and control on high-risk groups are enhanced by periodically collecting core indexes and linking risk upgrading and priority intervention, so that the tumor radiotherapy rehabilitation nursing is changed from passive response to active pre-judgment, and the rehabilitation nursing efficiency is improved. And the overall quality of rehabilitation nursing after radiotherapy and the prognosis effect of a patient are optimized.

The invention discloses a prognosis evaluation method and system for diffuse large B-cell lymphoma. According to the method, firstly, a gene module closely related to lipid metabolism is screened from DLBCL transcriptome data through weighted gene co-expression network analysis (WGCNA), and then eight key prognosis genes including FNDC1, IL22RA2, C15orf48, OMD, MFAP2, BC017398, CXCL6 and TNFAIP6 are identified from the module by adopting multi-step regression analysis (single factor Cox, LASSO and multi-factor Cox). A risk scoring model is constructed based on the expression levels and regression coefficients of the genes, and DLBCL patients can be divided into a high-risk group and a low-risk group with significant survival differences. The risk score and the clinical pathological factors are further integrated to construct a column graph, and individualized survival probability prediction can be achieved. The invention further provides a corresponding prognosis evaluation system, electronic equipment and a storage medium. An independent data set verifies that the prognosis model has excellent prediction performance and clinical practical value.

The invention discloses a sugar chain marker combination for predicting the recurrence risk of a gastric cancer patient after treatment and application of the sugar chain marker combination. The invention discovers that the levels of six specific N-sugar chains (NGA2F, NG1A2F, NA3, NA3Fb, NA4 and NA4Fb) in blood are obviously related to relapse during definite diagnosis of gastric cancer for the first time. On the basis, a gastric cancer recurrence risk prediction model GC-GPSS is constructed through Cox regression. According to the model, individual risk scores are calculated by using a quantification formula containing a fixed regression coefficient (beta), and patients are divided into a high-risk group and a low-risk group according to a preset threshold. A verification result shows that the model can effectively distinguish patient groups with different recurrence risks. The marker combination, the prediction model and the system provided by the invention provide a brand new tool for realizing earlier and more objective recurrence risk assessment in the early stage of gastric cancer diagnosis and treatment, and have important clinical application value.

The invention provides a loan default prediction method and device, and relates to the technical field of risk assessment. The method comprises the steps that historical data are acquired, the historical data comprise multiple pieces of historical user loan data, and each piece of historical user loan data comprises basic features and a corresponding loan default label; clustering the historical data, and performing default risk assessment on each clustered data set to obtain a risk level of each clustered data set; determining an oversampling strategy of each clustering data set based on the risk level of each clustering data set; on the basis of the oversampling strategy of each clustering data set, oversampling is carried out on each clustering data set; and based on the balanced data set, training a preset default prediction model to obtain a loan default prediction model for loan default prediction. And blind user manufacturing in a high-risk group is avoided, so that the model is more stable, and the loan default prediction effect is improved.

Disclosures herein are directed to methods and compositions for predicting high- and low-risk liver disease 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 HCC 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 diseases.

The invention discloses a differential privacy protection method and system based on dynamic gradient, and the method comprises the steps: 1, monitoring the gradient features of back propagation in real time in the training process of a target neural network model, and calculating a dynamic sensitivity zoom factor based on the gradient features; 2, identifying the type of the network hierarchy in the target model based on a three-level progressive judgment method; and step 3, setting a noise scaling factor according to the network hierarchy, selecting a noise type according to the intensity requirement of the network hierarchy for privacy protection, obtaining a noise scale corresponding to the noise type by using the dynamic sensitivity factor and the noise scaling factor, constructing noise according to the noise scale, and injecting the constructed noise into the corresponding network hierarchy to obtain the privacy protection. Obtaining a noise-added target model; and step 4, predicting input target data based on the neural network model after noise addition. According to the method, the protection effect of the model is improved, and stealing attacks aiming at genetic privacy and high-risk group identities can be effectively defended.

The invention discloses a product recommendation method and device, computer equipment and a storage medium, belongs to the technical field of artificial intelligence, and is applied to health management and health insurance product recommendation scenes. The method comprises the following steps: firstly, carrying out precise modeling on dynamic changes of user physiological indexes by utilizing time sequence analysis, and obtaining an interpretable trend and an abnormal mode, so as to realize early recognition and quantitative evaluation of health risks; secondly, carrying out traceable and explainable multi-modal fusion on an abnormal index quantification result and individualized multi-dimensional information (family medical history, medical behaviors and the like), and outputting a comprehensive health risk score; thirdly, through efficient clustering analysis of historical risk data, a clear group portrait of similar risk groups is formed, and robustness and expandability of grouping intervention and personalized matching are improved; and finally, based on a matching algorithm of similar risk groups and a rich product library, personalized recommendation of health insurance products is realized, and the user satisfaction and the conversion rate are improved.

The invention discloses a system and equipment for predicting colorectal cancer and progression thereof. Based on the found expression level difference of AXIN2 and / or EPHB2 in colorectal cancer and the progression thereof, the invention provides a colorectal cancer and progression prediction method, system and equipment, a computer readable storage medium and a computer program product, high-risk groups can be efficiently and accurately screened out from common groups, and colonoscopy is further completed in time. While the economic burden caused by endoscopic screening is relieved, patients suffering from colorectal adenoma and early colorectal cancer are screened out, and the method has great significance in early diagnosis and treatment of colorectal tumor and further remarkable reduction of the morbidity and mortality of colorectal cancer.

The present invention relates to a novel microorganism and a use thereof for preventing, alleviating, or treating intestinal diseases. The strain according to one embodiment has effects of inhibiting intestinal atrophy and reducing bloody stools, and thus is useful for the prevention, amelioration, or treatment of intestinal diseases. In addition, since the strain is significantly reduced in the patient group, the strain can be used for early diagnosis of intestinal diseases or selection of a risk group.

The invention discloses a lung cancer high-risk group screening questionnaire cognitive calculation correction method and system. The method comprises the following steps: acquiring questionnaire text data of a subject; constructing a BERT-QA model based on the clinical term knowledge graph, analyzing questionnaire text semantics, and extracting structured risk factor data; mapping the risk factor data to a topological space, and constructing a risk topological representation; calculating a dynamic weight coefficient of each risk factor based on an information entropy principle, and generating a risk assessment value of entropy weight optimization; carrying out calibration in combination with regional pollution map data; according to the method, deep learning, topology and fuzzy logic technologies are fused, intelligent analysis of questionnaire texts and dynamic weight calculation of risk factors are realized, environmental pollution factors are effectively integrated, the screening accuracy and individuation level are improved, and the method is suitable for large-scale popularization and application. The method provides technical support for accurate identification and early intervention of lung cancer high-risk groups, and has important clinical application value.

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 invention discloses a biomarker combination for predicting PCI postoperative LVR of an STEMI patient and application of the biomarker combination, and belongs to the technical field of biology. The research discovers that in a left ventricular remodeling (LVR) group and a non-left ventricular remodeling group after percutaneous coronary intervention (PCI) of a patient suffering from acute ST segment elevation type myocardial infarction (STEMI), a left ventricular endocardial lower myocardial overall longitudinal strain (GLS internal) value, a lung B-line integral, a serum sFRP5 level and Apelin-13 show significant differences. Through combined prediction analysis, it is found that the GLS internal value, the lung B-line integral, the serum sFRP5 and the Apelin-13 have excellent prediction performance on whether the LVR occurs after the PCI operation of the STEMI patient or not, high-risk groups, suffering from the LVR after the PCI operation, of the STEMI patient can be accurately and efficiently recognized, intervention is conducted as soon as possible, and the life quality of the patient is expected to be improved.

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 invention belongs to the technical field of medical biological detection, and particularly relates to a gene marker combination for oral mucosa leukoplakia canceration risk and application of the gene marker combination. The gene marker combination is a combination of TP53, CASP8 and CYP2B6 genes and is obtained by screening through a WES technology, the predicted AUC of the gene marker combination reaches 0.813 (95% CI: 0.710-0.916), and the performance of the gene marker combination is remarkably superior to that of traditional pathological grading. Through Cox risk regression analysis, a high-risk group as an independent factor has significant statistical significance and is superior to traditional indexes such as age and diseased regions. The invention further provides an oral mucosa leukoplakia canceration risk prediction kit and prediction system derived from the gene combination.

The invention discloses application of a lactic acid modified gene ENO1 inhibitor in preparation of a medicine for treating nasopharynx cancer, and relates to the technical field of biological medicines. A head and neck squamous cell carcinoma prognosis model containing seven lactylation related genes is constructed by analyzing a TCGA database, the model can effectively distinguish patient risks, the total lifetime of a high-risk group is remarkably shortened, and a risk score is an independent prognosis factor; the functions of the key gene ENO1 are deeply studied through in-vitro experiments, and experimental results show that silencing of the ENO1 gene in nasopharynx cancer 5-8F cells causes up-regulation of cell pan-lactylation level, up-regulation of MMP2 expression and down-regulation of E-cadherin expression, and migration and invasion ability of tumor cells is significantly enhanced. The prognosis model provided by the invention has an important clinical prediction value, inhibition of ENO1 can promote tumor progression through abnormal lactylation, and a new thought is provided for taking ENO1 as a treatment target.

The invention belongs to the technical field of tumor prognosis prediction, and relates to a colorectal cancer allotropic liver metastasis prediction method and system.The method comprises the steps that colorectal adenocarcinoma cases are collected, the volume of an intra-tumor region-of-interest is delineated and automatically expanded to generate the volume of a peritumor region-of-interest, image omics characteristics are extracted through a pyradiomics packet, core omics characteristics are screened out, and the colorectal cancer allotropic liver metastasis prediction result is obtained. The method comprises the following steps: respectively constructing an intratumoral model and a peritumoral model, analyzing and integrating intratumoral and peritumoral core omics characteristics through logistic regression to form a combined radiomics model, integrating the combined radiomics model and clinical risk factors, establishing a column diagram for predicting the non-hepatic metastasis lifetime, and dividing patients into a low-risk group and a high-risk group according to a column diagram score threshold value; according to the method, LMFS prediction results of 1-5 years can be quickly output, and 0.6911 is set as a standardized risk stratification cut-off value so as to support risk stratification and personalized treatment decision of a patient.

One aspect relates to a method for providing information for predicting a risk group for developing Alzheimer's disease dementia or a risk group for early onset of Alzheimer's symptoms, or a risk group for developing amnestic mild cognitive impairment and / or a positron emission tomography (PET)-positive risk group for amyloid β deposition, based on a European population-East Asian data. According to one aspect, the method makes it possible to accurately predict a risk group for developing Alzheimer's disease dementia or a risk group for early onset of Alzheimer's symptoms, or a risk group for developing amnestic mild cognitive impairment and / or a positron emission tomography (PET)-positive risk group for amyloid β deposition by using only at least 12 single-nucleotide polymorphisms, and the ability to predict the risk groups is further enhanced when up to and at most 80 additional single-nucleotide polymorphisms are used. In addition, the method further includes identifying age, sex, years of education, and APOE genotype as indicators, and thus the ability to predict the risk groups is further enhanced, and the risk groups can be predicted at an early stage with high accuracy.

The invention discloses a fracture risk prediction method and system, and relates to the technical field of health management. According to the prediction method, serious sarcopenia is selected to diagnose and assess the sarcopenia state, the ratio of monocytes to lymphocytes reflects the immune inflammation level, the prediction model is constructed in combination with the femoral neck bone mineral density T value, and the fracture risk influence factors are comprehensively covered; and the risk identification capability on special high-risk groups with sarcopenia, immune dysfunction and the like is effectively improved. And secondly, only three target independent variables are selected, and the three target independent variables are indexes which can be directly obtained or simply calculated in clinical routine examination, so that the data collection difficulty is greatly reduced. Meanwhile, the prediction model is visualized through a column diagram, complex calculation is not needed, a clinician can quickly inquire the fracture risk score through the actual value of the target subject, and the method is suitable for being efficiently applied to conventional outpatient service, hospitalization and other scenes.

The invention relates to the technical field of intelligent traffic, and particularly discloses a highway village pedestrian and non-motor vehicle risk identification method and system, and the method comprises the steps: firstly, sensing traffic participants through multispectral and thermal imaging data fusion, and generating a state set; then, constructing a dynamic interaction relation topology, and calculating a motion trend vector and an interaction strength coefficient; calculating an initial risk factor based on road region division, and updating a comprehensive influence factor through a risk propagation mechanism; and finally identifying a risk group and a risk chain, and generating graded early warning information. Through multi-source data fusion and dynamic topology analysis, the problem of insufficient group risk and linkage risk identification caused by lack of semantic understanding in the prior art is solved, the crossing from single target detection to complex interaction scene risk assessment is realized, and the accuracy of risk identification and the timeliness of early warning are improved.

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 invention provides target sections S6-1, S6-2 and S6-3 for identifying multiple cancer species and application of the target sections S6-1, S6-2 and S6-3. A group of unprecedented gene methylation sites are successfully identified through an innovative screening strategy and large-scale cross-cancer omics data analysis. The invention also comprises a detection reagent (such as a primer) and a kit for detecting the target segment of the genome. The methylation degrees of the target segment between a plurality of cancer tissues and para-carcinoma tissues have significant differences, so that the subject can be judged to belong to tumor high-risk groups. The cancer tissue and para-carcinoma tissue differential methylation state presented by the target segment can be widely applied to various different types of cancers.

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.

The invention relates to a biomarker for diagnosing rheumatoid arthritis and application of the biomarker, and belongs to the technical field of biological medicines. The invention finds that the concentration of lipopolysaccharide and diamine oxidase in the plasma of a patient with rheumatoid arthritis (RA) is obviously higher than that of a healthy control group; furthermore, the invention also discovers that three proteins from plasma exosomes, namely phospholipid flip enzyme 1 (PLSCR1), keratin 1 (KRT1) and complement C1Q C chain (C1QC), have a good diagnosis effect on rheumatoid arthritis, and the diagnosis accuracy of rheumatoid arthritis can be remarkably improved by performing combined diagnosis on lipopolysaccharide and diamine oxidase with the three proteins. Therefore, when the biomarker is applied to preparation of RA diagnosis products, a core basis can be provided for accurate diagnosis of RA and generation of a risk assessment system, and the biomarker has transformation application value in the aspects of early diagnosis of RA and screening and accurate prevention and treatment of high-risk groups.

The application relates to the technical field of medical artificial intelligence, and particularly discloses an acute pancreatitis chronicity risk prediction system and method and a storage medium, the system comprises a parameter acquisition module, a model training module, a risk prediction module, a risk stratification module, an AI analysis module and an interface interaction module, the parameter acquisition module is used for acquiring clinical parameters and enhanced variables of a target patient; the model training module is used for constructing a risk prediction model and an enhanced risk prediction model; the risk prediction module is used for predicting a prediction risk probability of the target patient in chronic pancreatitis after being discharged from hospital; the risk stratification module is used for dividing the target patient into a low-risk group, a medium-risk group or a high-risk group, and generating a risk stratification result; the AI analysis module is used for generating a clinical interpretation report; and the interface interaction module is used for displaying the prediction risk probability, the risk stratification result and the clinical interpretation report, so that high-risk patients with chronic pancreatitis can be effectively identified at an early stage, and a personalized follow-up scheme can be assisted in being formulated.