229 results about "Risk groups" patented technology

The invention relates to the technical field of disease prediction, in particular to an intelligent risk prediction method based on a neural network, and the method comprises the steps: collecting blood glucose and body mass index data, carrying out the clustering analysis, and constructing a multi-risk group feature; a risk transfer track before attack is recognized by combining a historical index change path and time sequence analysis; and calculating an individual risk coefficient by using deep learning, and comparing the individual risk coefficient with a risk threshold to generate early warning information, thereby realizing dynamic accurate evaluation. According to the method, by collecting the blood glucose level and body weight index data and conducting standardization processing, risk group division based on health index clustering can be achieved, a group recognition system with health feature differentiation is constructed, and the structured understanding of the individual health trend is enhanced. And on the basis of the clustered group, extracting a fluctuation path of individual historical indexes by means of a time sequence mode, identifying key transfer characteristics before disease attack, and enhancing the traceability of the disease formation process.

The invention provides an electrocardiogram data analysis method, device, medium and equipment based on multi-modal fusion, which are applied to early warning of acute coronary syndrome (ACS). The method comprises the following steps: acquiring time sequence data of electrocardiosignals and ballistocardiogram signals as well as structural data such as heart rate, respiratory rate and oxyhemoglobin saturation by using a portable device, and preprocessing the data; constructing a double-branch global and local interactive feature extraction model, respectively extracting local and global features of the electrocardiosignal and the ballistocardiogram signal, and simultaneously bridging the isomerism of the two signal types; after feature mapping is carried out on the structural data, inputting the structural data and features of the time series data into a feature fusion model; and finally inputting the fused features into a disease early warning model, outputting an ACS risk probability, and comparing the ACS risk probability with a preset threshold value to realize real-time monitoring and early warning. The method is of great significance to prevention of ACS and guarantee of life health of high-risk groups with cardiovascular and cerebrovascular diseases.

The invention relates to the field of intelligent data analysis, and particularly discloses an intelligent data analysis processing system and method based on deep data mining, which uses an artificial intelligence technology based on the deep learning field to perform feature extraction and coding on original data of a medical record to be detected and living habit records of a patient. And a classification result of whether the patient is a high-risk group of diabetic complications is obtained. Therefore, by intelligently judging whether the patient is a high-risk group of diabetic complications or not, illness state delay can be effectively avoided, and the best opportunity for treating diabetes mellitus is missed.

The invention provides a self-adaptive clinical trial design method based on machine learning, relates to the technical field of clinical trials, and adopts a method integrating reinforcement learning, causal inference and data enhancement technologies to predict an optimal treatment scheme and adjust a distribution proportion in real time through a dynamic optimization module, and limits high-risk treatment selection in combination with dynamic ethical constraints. The problems that a traditional clinical test preset rule lacks real-time performance and neglects patient safety are solved, a data collection and enhancement module generates virtual patient data conforming to a causal relationship, real and virtual data are combined to form a unified data set, a high-quality data set is constructed, the data size is effectively expanded, the statistical power of a small sample scene is improved, and the accuracy of a clinical test result is improved. The termination rule module of comprehensive statistics, ethics and resources shortens the test period through weighted evaluation, significantly reduces the risk of improper allocation of high-risk groups, enhances the benefit of patients, ethics compliance and test result reliability, and expands the application range in rare disease research and personalized medical treatment.

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 an abnormal data identification method and device, computer equipment and a storage medium. The method comprises the following steps: constructing a user social network graph according to user communication information; dividing risk groups according to the user login information and the user transaction information to obtain high-risk groups; performing association marking in the user social network graph according to the high-risk group to obtain a potential abnormal group; marking a target user to be identified for the user login information and the user account information of the potential abnormal group; and obtaining the logout times of the to-be-identified target user, and marking the to-be-identified target user as target abnormal data according to the logout times. The method and the device can be applied to financial anti-fraud application scenes, target abnormal data with fraud risks in users can be effectively and accurately identified, and the identification reliability is improved.

The invention discloses a schizophrenia early warning and evaluation system based on human brain multi-region signals, and relates to the technical field of early warning and evaluation, and the system comprises a neural connection analysis unit which is used for recognizing neural connection characteristics of a brain network based on biomarker characteristics, comparing the neural connection characteristics with a database, and determining the neural connection characteristics of the brain network; clustering individuals with similar neural connection features according to a comparison result to generate a risk group; the emotion perception analysis unit is used for acquiring facial emotion states of the risk group in a preset time period and predicting evolution trends of neural connection features in different facial emotion states; and the early warning evaluation unit is used for inputting the evolution trend of the neural connection characteristics into a pre-constructed evaluation model, outputting a risk level evaluation result and formulating an early warning measure based on the risk level evaluation result. According to the method, neural connection features and emotion perception analysis are combined, so that early-stage neural connection abnormity and emotion turning intervals of schizophrenia can be accurately identified and positioned.

The invention discloses an ultrasonic anesthesia positioning method and system based on AI preoperative evaluation and a storage medium, and relates to the technical field of medical assistance. The method comprises the following steps: comprehensively collecting body health data of a patient, including a physical examination result, a laboratory examination result and a medical history, reasoning the body health data of the patient based on an operation risk knowledge graph model, and determining an operation risk level; if the operation risk level is higher than a preset risk level value, the operation risk is evaluated again in combination with past operation case information, and if the operation condition is met, the crowd is judged to be the high-risk crowd of the operation; an ultrasonic image of an in-vivo structure of an operation high-risk crowd is collected by utilizing an ultrasonic technology, the ultrasonic image is recognized by utilizing an image recognition AI algorithm, the relationship between an anatomical position and an adjacent part is determined, and then an anesthesia position is determined. The medical quality and the service level can be improved, and safer and more efficient surgical treatment experience can be brought to patients.

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.

The invention relates to an intelligent method and system for rapid screening and triage of aortic dissection. The method comprises the following steps: collecting text information data sets indicating chief complaint symptoms, first symptoms, past medical history and high-risk signs; based on the text information data set, risk factor identification is carried out by using a feature extraction and classification algorithm combining natural language processing with machine learning; through a correlation analysis technology, correlation between the risk factor and the chief complaint symptom and between the risk factor and the first symptom is calculated, and whether the patient belongs to an AD high-risk group or not is preliminarily evaluated by setting a threshold value according to correlation strength; starting a first-aid green channel for the target patient which is preliminarily assessed as AD high risk, measuring blood pressure of four limbs, and recording generated blood pressure data; comparing the blood pressure data of the target patient with a preset threshold value, and determining the possibility that the target patient has suspected AD through logical judgment; and performing priority triage based on the suspected AD possibility evaluation result, the severity of the clinical symptoms and the availability of the medical resources.

The invention provides an intelligent processing method, system, equipment and medium for high-risk and newly-onset definite diagnosis crowds of hypertension, and belongs to the technical field of hypertension detection.The method comprises the steps that patient examination data are collected and serve as an original data set; preprocessing the original data; constructing a corresponding parameter group; mapping the family history score into a risk quantification function through a dynamic weight coefficient Wi; summing the product of the dynamic weight coefficient Wi and the quantization function Fi to obtain a total risk score; determining whether the patient is a high-risk population based on the diagnosis code and the total risk score; and pushing the high-risk crowd data to a management center server. According to the method, the data is accurately pushed to a corresponding platform or a storage library, so that safe and effective utilization of the data is guaranteed, and sharing and collaboration of regional medical data are realized.

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 provides a liver function data detection method and system for liver cancer high-risk groups, and relates to the field of health management. Context snapshots based on user IDs are created and updated in real time in a high-speed memory cache region, and a user diagnosis type, a medication state and a medication timestamp are combined to detect the liver function data of the liver cancer high-risk groups. According to the method and the device, the liver function data is subjected to differential judgment on the basis of the preset logic tree, so that the problems of misinformation and missing report caused by lack of context information in the prior art are effectively solved, the liver function data can be subjected to differential judgment accurately in real time according to the diagnosis type and the medication state of the user, misinformation and missing report are effectively avoided, and the user experience is improved. And the accuracy and the reliability of the alarm system are improved.

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 peripheral blood and CT-based liver transplantation recipient sarcopenia classification method and device, and the method comprises the steps: carrying out the single-factor COX regression preliminary screening of collected peripheral blood biochemical indexes of liver transplantation recipients, and obtaining potential prediction factors related to sarcopenia; performing LASSO regression screening on the potential predictive factors to obtain core predictive factors related to sarcopenia; multiplying each core predictive factor by a regression coefficient calculated in LASSO regression, performing accumulation calculation to obtain an LASSO + COX model score, and dividing liver transplantation recipients into different risk groups according to the size of the LASSO + COX model score; and carrying out definite diagnosis on sarcopenia according to a risk group division result in combination with a CT image examination result. According to the method, a new strategy of peripheral blood prediction preliminary screening and high-risk CT confirmation is realized, the radiation risk, cost and detection threshold are reduced, and the sarcopenia management efficiency is improved.

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 belongs to the technical field of medical treatment, and discloses an auxiliary decision-making method for accelerating rehabilitation surgery clinical path multi-decision multi-objective optimization. The method comprises the following steps: constructing a data set of preoperative states and medical decisions of a patient; unsupervised clustering is adopted, the clustering method is beneficial to dividing the patients into different phenotypes and risk levels according to unique features of the patients, and the obtained clusters provide valuable insights for heterogeneity of patient groups. And then, according to the decision data of each clustering phenotype, constructing a Bayesian network model, and adding some regulations in the construction process to trim the network, so that the constructed Bayesian network model better accords with clinic. And finally, inferring the state posterior probability of each node by using the Bayesian network, and inferring a decision scheme of each risk group by using the support degree so as to accelerate the postoperative rehabilitation process of the patient, reduce the hospitalization time of the patient and reduce the hospitalization cost.

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 mainly provides a noise hearing loss assessment device, which comprises a screening module, a collection module, a calculation module, an analysis module and an assessment module, and is characterized in that the assessment device performs single-factor regression analysis on key information of a hearing loss group and a hearing non-loss group and accumulated noise exposure; obtaining difference factors causing hearing loss, and performing multi-factor regression analysis according to the difference factors causing hearing loss to obtain independent risk factors causing hearing loss; according to the independent risk factors causing hearing loss, the column diagram is constructed and used for evaluating hearing loss of a plurality of to-be-detected objects, the evaluation device has good sensitivity and specificity when being used for evaluating hearing loss risks caused by occupational noise exposure, early recognition of high-risk groups can be achieved, effective individual intervention measures can be taken in time, and the evaluation efficiency is improved. The occupational health of workers is protected.

The invention provides a biomarker for predicting the preeclampsia risk, application of the biomarker and a method for detecting whether an object has the preeclampsia risk or not. According to the method for detecting whether the object has the preeclampsia risk, preeclampsia risk prediction is carried out on all pregnant women in the early and middle stages of pregnancy, whether preeclampsia high-risk groups exist or not is not distinguished, and risk prediction can be carried out before symptoms occur; different types of free RNA in blood are associated with preeclampsia, the biomarker is used for constructing a preeclampsia prediction model in at least 300 samples, and the specificity of the preeclampsia prediction model during pregnancy can be improved to 85% or above.

The invention provides a tumor treatment prognosis evaluation method and system based on machine learning. The method comprises the following steps: S1, analyzing the influence of each immune cell subset of peripheral blood of a patient on tumor treatment responsiveness through a single-factor logistic regression model; s2, based on the total lifetime of the patient, constructing a multi-factor COX risk scale model by using the immune cell subpopulation, and calculating a peripheral blood immune pattern risk score of the patient; and S3, dividing the patients into a high-risk group and a low-risk group according to the median value of the risk score. According to the tumor treatment prognosis evaluation method and system based on machine learning, the peripheral blood immune pattern of the advanced tumor patient at different time points can be evaluated while noninvasive and dynamic material taking is achieved, then the influence of small-dose rhythm chemotherapy on prognosis of the advanced tumor patient is accurately and dynamically predicted, and the method and system are suitable for clinical transformation and popularization and have good application prospects. And a reference value is provided for treatment scheme selection of advanced tumor patients.

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 an anterior cervical approach postoperation adjacent segment degeneration occurrence prediction model for a cervical spondylosis patient, and belongs to the field of cervical spondylosis postoperation prediction. The invention discloses a model for predicting occurrence of anterior cervical approach postoperative adjacent segment degeneration of a cervical spondylosis patient. The model comprises a classifier and risk factors input into the classifier, the classifier is used for preprocessing and sketching a region of interest by acquiring a medical image of a target region of a patient, then extracting image omics features, inputting the image omics features into various machine learning models to calculate a C index, and selecting an optimal machine learning model as the classifier based on the C index; the risk factors are obtained through screening by performing single-factor and multi-factor logistic regression analysis on clinical features. Through the model, a clinician can be helped to avoid related risks to the greatest extent when a cervical spondylosis operation scheme is made, meanwhile, high-risk groups with ASD after an operation are recognized, and a basis is provided for implementation of targeted follow-up visit and early intervention.

The invention relates to the technical field of safe lease crowd assessment, is used for solving the problems that a traditional product lease platform lacks multi-dimensional data monitoring in the aspect of user safety assessment and risk user groups are inaccurately identified, and particularly discloses a safe lease crowd assessment system of a product lease platform. Comprising a data acquisition module, a server, a data analysis module, a risk assessment module, a delimiting analysis module, a verification analysis module and a display terminal. According to the method, the corresponding risk values are determined by monitoring the basic data parameters, the external data parameters and the behavior data parameters of the user, the lease risk assessment model is constructed based on the risk values, the comprehensive risk assessment value is calculated to determine the risk type state, and the lease risk assessment model is constructed according to the user characteristics corresponding to the risk type state. The features of different risk groups are accurately identified, and the target feature information interval is determined accordingly, so that accurate classification of user groups is realized, lease risk assessment accuracy is improved, and lease transaction safety is effectively guaranteed.

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.