164 results about "Risk factor" patented technology

The invention discloses a personal obesity risk prediction system and method based on AI of big data, and belongs to the field of medical health, and the system comprises a data collection module, a multi-dimensional feature construction module, a risk label dynamic generation module, a model training and risk prediction module, a credibility evaluation and calibration module and the like. The system collects multi-source heterogeneous data through wearable equipment, a biochemical interface and a health platform API (Application Program Interface), uniformly encodes the multi-source heterogeneous data into a standard time sequence and then constructs behavior-metabolism-environment coupling characteristics. And performing joint modeling on the dynamic features and the labels by adopting a graph neural network in combination with causal factorization, and outputting an individual obesity risk prediction result. The result credibility is improved through Monte Carlo Dropout and a confidence interval calibration mechanism, and calibration information is fed back to a feature construction link to optimize a modeling strategy. Finally, the key risk factors are presented in the form of a visual thermodynamic diagram and a causal path diagram, and an individualized intervention suggestion vector is generated. The method has the beneficial effects of improving prediction accuracy and enhancing individual intervention pertinence.

The invention provides a knowledge graph-based public security multi-source risk factor association identification analysis method, which relates to the technical field of risk identification, and comprises the steps of obtaining multi-source risk factor data, extracting information from unstructured data, constructing an initial association network, performing feature analysis and calculating a similarity matrix; and the close association subgroups are identified through community discovery, a multi-level association network is constructed, a conduction path is analyzed, a weight is calculated, and finally risk early warning information is generated. According to the invention, the complex association between public security risk factors can be effectively identified, and the risk prediction accuracy is improved.

The invention discloses a children obesity early warning method and system based on obesity risk factor analysis, and the method comprises the steps: carrying out the feature screening of collected children multi-dimensional health related data containing parental information, birth history and lifestyle, and recognizing the important risk factors and weights of children obesity; important risk factors are supplemented through literature review; the method comprises the following steps: formulating scale questions including parental information, birth history and lifestyle based on important risk factors and weights in combination with clinical expert opinions, assigning scores to the scale questions, dividing according to a scale score result to form children obesity risk prediction levels, and providing personalized health management schemes for subjects with different risk levels. The method is low in cost, easy to implement, high in compliance and based on non-invasive characteristics, and is particularly suitable for early obesity screening and early warning of 3-6-year-old children.

The invention relates to the technical field of information management, in particular to an underground pipeline gallery risk monitoring method and device. According to the method, a heterogeneous sensing terminal network is used for sensing a target underground pipeline gallery according to a fast and slow time sequence, and a pipeline gallery monitoring anchor point set is determined; performing cross-modal interaction on a heterogeneous sensing terminal network based on the pipeline and pipe gallery monitoring anchor point set to obtain a pipeline and pipe gallery monitoring anchor point multi-modal fusion feature set; performing anchor point risk assessment according to the pipeline and pipe gallery monitoring anchor point multi-modal fusion feature set, and obtaining a pipeline and pipe gallery monitoring anchor point risk factor set according to an assessment result; and when the pipeline and pipe gallery monitoring anchor point risk factor set is greater than or equal to a preset anchor point risk factor threshold value, triggering a risk early warning instruction. According to the method, accurate identification and dynamic early warning of the key risk points of the underground pipeline gallery are achieved through fast and slow time sequence sensing and cross-modal feature fusion processing based on the monitoring anchor points.

The invention relates to the technical field of diabetes prediction, in particular to a method for predicting early diabetes based on health data, and the method comprises the steps: carrying out the data collection and preprocessing of an individual health system, and obtaining diabetes risk related data; performing off-line calculation on the data by using a feature correlation analysis technology, and extracting a diabetes prediction feature set; performing risk assessment on the feature set through a preset diabetes prediction model to generate a diabetes risk prediction result; key risk factors are extracted, a risk factor classification model based on a random forest is constructed, risk attribution analysis is carried out, and main risk factors are identified; then, comprehensive assessment is carried out on the main risk factors in combination with confidence propagation analysis and risk path analysis technologies, and a core risk source is positioned; the individual diabetes risk portrait is constructed according to the core risk source, the relationship among the risk factors is analyzed, a personalized early intervention scheme is formulated, and the comprehensiveness, precision and interpretability of early diabetes prediction are remarkably improved.

The invention discloses a hospital pharmacy inventory intelligent management system and method, and belongs to the technical field of data management. Medicine basic information and multiple batches of inventory records are acquired; an application risk grade R is extracted based on the medicine application label; combining the remaining validity period, the stability parameter and the risk factor to construct a batch ex-warehouse priority scoring model P; generating a sorting ex-warehouse list and executing allocation and ex-warehouse operation; when it is monitored that the medicine is in time or the inventory is abnormal, dynamically adjusting the score and updating the sorting result; according to the invention, fine control of the drug delivery sequence can be realized, clinical availability of high-risk-purpose drugs is guaranteed preferentially, and inventory use efficiency and medication safety are improved at the same time.

The invention relates to the technical field of food safety data processing and intelligent early warning, and particularly discloses a safety early warning method and system based on food safety inspection data. The method comprises the following steps: acquiring multi-source food safety inspection data, performing structured preprocessing, and constructing a unified feature vector space; extracting space-time correlation characteristics to generate a dynamic risk factor sequence; inputting the sequence into an interpretable fusion model for score calculation, and generating a risk score matrix; fusing the product information to generate an attribute labeling vector, constructing a food safety knowledge graph and carrying out causal reasoning; and generating early warning information and a risk tracing path based on a reasoning result, and carrying out reverse correction and model updating on the risk factors. According to the method, multi-source data modeling, causal relationship reasoning and a model self-learning mechanism are fused, dynamic identification and closed-loop early warning of food risks are realized, and the method has high adaptability and interpretability.

The invention provides a dynamic prediction algorithm for monitoring late-onset infection of a premature infant and an upgrading system. The dynamic prediction algorithm for monitoring late-onset infection of the premature infant and the upgrading system comprise the following steps: a, collecting clinical data of the premature infant, including birth weight, gestational age, 1-minute and 5-minute Apgar scores, right hand perfusion index, lower limb perfusion index and other related clinical information, and b, determining the early-onset infection of the premature infant through medical history collection and physical sign analysis. The method comprises the following steps: collecting data of 11 classification independent variables: prenatal antibiotic use conditions (existence and absence); according to the dynamic prediction algorithm for monitoring late-onset infection of the premature infant and the upgrading system, the infection risk index is effectively calculated through high-risk factors analyzed by the Lasso regression model in combination with clinical basic data of the premature infant, early warning of infection of the premature infant is provided for medical staff, and the accuracy and timeliness of infection prediction are remarkably improved. Besides, the system can automatically remind medical staff to intervene the high-risk child patient through red warning, so that the death rate caused by delayed discovery and delayed treatment is reduced, and the clinical intervention effect is improved.

The invention relates to the technical field of biomedical engineering, and discloses a VTE system execution method and system combined with a standardized scale, and the method comprises the steps: carrying out the standardization processing of original data, and obtaining the standardized data; constructing a risk intensity flow; carrying out integral operation to obtain an accumulated integral value of the risk factor; calculating a dynamic risk trajectory; fusing the baseline score value of the standardized scale with the dynamic risk trajectory to obtain a total risk trajectory; calculating an instantaneous risk probability, and judging whether VTE risk early warning is triggered or not according to a self-adaptive threshold value; according to the method, continuous quantitative monitoring of VTE risk factors is achieved by constructing the body position, anesthesia, hemostatic and circulation parameter four-dimensional risk intensity flow, early recognition and timely early warning of VTE risks are achieved through double mechanisms of continuous triggering and acute triggering, and the accuracy, the real-time performance and the individualized level of risk assessment are remarkably improved.

The invention discloses a breast cancer risk prediction method based on machine learning and multi-dimensional data, and belongs to the technical field of medical health information.The method comprises the steps that based on an NHANES database, diet, living habits and other information are collected, and a data set is formed; performing pretreatment; screening meaningful data features by using three feature selection methods of LASSO regression, mRMR and forward selection, and obtaining a final feature data set after intersection; dividing a training set and a test set; establishing a risk prediction model by using an SVM machine learning method, and learning the training set; performing model performance analysis on the test set to obtain a risk prediction probability of a final training model; the method has the advantages of multi-source data integration, high-precision prediction, personalized evaluation, dynamic updating and the like, risk factors of the breast cancer can be effectively mined, theoretical support is provided for prevention and treatment of the breast cancer, high-risk group screening is guided, morbidity reduction is assisted, early diagnosis and early treatment are achieved, and development of female health undertaking is promoted.

The invention relates to the field of medical health management, and discloses a thyroid-oriented whole-cycle health management method, which comprises the following steps of: firstly obtaining thyroid multi-source information data of a target population to form a data set, then extracting fusion features to generate a time sequence feature vector, and analyzing and calculating occurrence risk factors and scores of feature dimensions in a preset time window according to the time sequence feature vector; a comprehensive risk score is obtained through time sequence analysis, risk grades are divided, high-risk individuals are marked, and a personalized screening strategy is generated; then combining historical data of high-risk individuals, analyzing key driving paths with high risk scores, associating an external knowledge base to predict recurrence frequency, and finally dynamically adjusting and optimizing screening and intervention strategies according to the recurrence frequency; precise evaluation, grading and personalized management of individual thyroid health risks are achieved, then risk key driving paths are found out, recurrence is predicted, and the strategy is dynamically optimized to improve the disease prevention and treatment effect.

The invention belongs to the technical field of medical data processing, and discloses a preoperative multi-complication risk prediction method and system based on structured clinical data, and the method comprises the steps: inputting the causal association between risk factors and complication nodes into the edge of a knowledge graph, calculating the statistical correlation between all complications, and supplementing the statistical correlation into the knowledge graph, and performing network embedding training on the knowledge graph to form a first-stage model, performing preliminary risk assessment on complications, modeling the knowledge graph in a graph neural network mode, and performing joint training with the first-stage model to form a second-stage model to output a final complication probability. According to the method, the interpretability and cross-domain consistency of the model can be improved through deep fusion of the medical knowledge graph and the multi-relational graph convolutional network, stability and calibration performance are still kept in a specialist with scarce sample size, and the problem that a traditional black box model cannot be interpreted is avoided; and the practical application value can be evaluated conveniently.

The invention discloses a medical synthetic data analysis method and device based on causal reasoning and a medium. The method comprises the following steps: generating medical synthetic data through a generator created according to a real clinical data set; analyzing the real clinical data set and the medical synthetic data to obtain a first causal diagram and a second causal diagram, and comparing the two diagrams to identify an abnormal causal edge corresponding to the medical synthetic data; determining a path corresponding to the abnormal causal edge in the first causal graph, extracting risk factors and corresponding results in the path, and respectively calculating an average processing effect and an effect difference value of the risk factors on the results in the real clinical data set and the medical synthetic data; and determining an analysis result of the medical synthetic data according to the effect difference value. According to the method, the medical synthetic data quality can be deeply analyzed, the fidelity of the synthetic data to a real-world clinical pathology causal mechanism is verified, the specific link of causal distortion is accurately positioned, and clear and executable guidance is provided for the correction data generation process.

The invention discloses a multi-classification risk prediction method and system for liver diseases based on machine learning, and relates to the technical field of biomedicine, and the method comprises the following steps: collecting fatty liver disease diagnosis results and biochemical indexes of a subject to form a training sample set, the method comprises the following steps: screening out biochemical indexes significantly related to fatty liver diseases through single-factor regression analysis, determining potential risk factors, carrying out multicollinearity test on the factors, screening out risk factors, constructing a plurality of machine learning classification models for training, and selecting a model with the best performance as a reference model. The contribution degree of each important risk factor is evaluated and sorted, classification significant factors are determined, the significant factors serve as classification metadata, a plurality of judgment models are trained, input factors are dynamically selected according to contribution values, and finally a fatty liver disease degree classification result is output, so that complex conditions under different sample features and clinical backgrounds are better handled; and the accuracy of prediction results is improved.

The invention discloses a foundation pit multi-source risk identification method and device, and the method achieves the quantitative description of a dynamic incidence relation between risk factors through the construction of a dynamic risk propagation network, the fusion of multi-source monitoring data, and the adoption of a causal and related joint modeling method, breaks through the limitation of a conventional static analysis model, and achieves the recognition of a foundation pit multi-source risk. Real-time risk identification and trend prediction in the foundation pit construction process are realized, and the foundation pit construction safety is ensured. Furthermore, according to the embodiment of the invention, a key risk factor and a high-risk propagation channel are quantitatively identified in combination with node centrality analysis and a weighted path identification mechanism, and the stability and traceability of a risk identification result are improved through a time aggregation mechanism, so that the accuracy and engineering adaptability of foundation pit risk control are remarkably enhanced.

The invention relates to the technical field of medical imaging omics analysis, in particular to a pancreatic perivascular fat image feature-based operation area inflammation degree grading method, which comprises the following steps of: determining clinical risk factors for pancreatic operation area inflammation degree grading through a statistical method; respectively segmenting ROI (Region of Interest) 1-6 in the preprocessed CT vein phase image and the preprocessed vein phase image through the combination of a TotalSegmentor segmentation model, an nnUNet segmentation framework and a region growing algorithm, and extracting cross-region image omics characteristics; and constructing an inflammation degree grading model based on the cross-regional radiomics characteristics and the clinical risk factors through a plurality of machine learning algorithms. According to the method, in the fusion model constructed by combining the risk factors and the radiomics characteristics, the clinical risk factors are found by using retrospective research, and meanwhile, the clinical risk factors and the radiomics characteristics are spliced by adopting an attention mechanism, so that the grading precision and efficiency of the inflammatory degree of the fusion model are ensured.

The invention discloses a complication prediction method and system based on an SHAP-random forest, and relates to the technical field of medical data mining. The method comprises the following steps: acquiring preoperative CT images and puncture path planning data, and calculating risk factors; performing numerical value standardization, nonlinear transformation and interactive feature generation on the risk factors to obtain feature vectors, calculating mutual information scores of feature values in the feature vectors, if the mutual information scores are greater than an experience threshold, retaining the feature values, and after traversal is finished, obtaining updated feature vectors; on the basis of a random forest model, taking the updated feature vector as an input value, and calculating a complication probability; quantizing the contribution degree of the characteristic value based on a Shapley value; according to clinical indexes, the risk threshold is dynamically corrected, the complication risk level is divided, and complication prediction is completed, the problems that a static threshold ignores the blood coagulation state difference of a patient and a black box model cannot provide a decision basis are solved, and the complication misjudgment probability is reduced.

The invention provides a T2DM risk factor differentiable causal discovery method fusing a weighting mechanism and multi-granularity search. Preprocessing and standardizing the original data; constructing a linear bow-free ADMG causal structure search space by using a structural equation model (SEM) and bow-free ADMG differentiable algebraic constraint; converting a discrete search problem into a continuous optimization task, and introducing a Pearson's correlation coefficient to improve a scoring function so as to correct mining deviation of potential causal edges; a regularization residual error iteration condition fitting method and a thickness multi-granularity search strategy are fused to avoid falling into local optimum, synchronous convergence of structure learning and strength estimation is achieved through an alternating iteration optimization structure and causal strength parameters, and finally a high-precision and stable ADMG causal structure is obtained. The method provides more reliable and interpretable theoretical support for T2DM causal relationship research, and can provide a new thought for diabetes prevention and treatment and research.

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.

The application provides a risk prediction model for 28-day mortality of an elderly sepsis patient after being transferred into an ICU and a construction method thereof, determines independent risk factors related to death within 28 days through single factor and multi-factor logistic regression analysis, makes a nomogram, and constructs the prediction model of the patent, and the comparison result of the AUC of the ROC curve of the standard APACHE II score and the frailty index (FI-lab) or other multi-index model in recent years indicates that the prediction model proposed in the patent has better performance.

The invention discloses a schizophrenia concurrent metabolic syndrome independent risk factor screening method and system, utilizes a binary logistic regression analysis method to analyze relevance between multi-source factors and concurrent MetS and screen out independent risk factors, and relates to the technical field of medical data processing. According to the schizophrenia concurrent metabolic syndrome independent risk factor screening method and system, the relevance between multi-source factors and concurrent MetS is analyzed through a binary logistic regression analysis method, the independent risk factors are screened out, and a decision tree model is constructed based on the reserved independent risk factors and used for visually displaying and predicting the risk of concurrent MetS of a patient. By comprehensively considering multi-aspect information of the schizophrenia patient and adopting independent risk factor determination and model construction operation, the independent risk factors causing MetS occurrence of the schizophrenia patient are accurately identified, the screening accuracy is improved, meanwhile, the level of MetS concurrent by the patient is predicted, subsequent clinical timely intervention is facilitated, and the risk of MetS occurrence of the schizophrenia patient is reduced. And the MetS concurrent probability of the patient is reduced.

The invention discloses a construction method of an AIS patient pre-hospital delay risk prediction model, and relates to the field of medicine. Comprising the following steps: data acquisition: acquiring data through a hospital electronic medical record system and telephone return visit, and performing anonymization processing on the data after approval by an ethical committee; model construction: performing inter-group comparison by using SPSS 29.0, then screening independent risk factors of pre-hospital delay by using binary Logistic regression, and finally constructing a column diagram prediction model by using an rms packet of R 3.6.3 software; and verifying the model, and evaluating the distinction degree through the area AUC under the working characteristic curve of the subject. The pre-hospital delay prediction model for the first-onset AIS patient, constructed by the method, has good distinction degree and accuracy, provides a basis for identifying the pre-hospital delay of the first-onset AIS patient, effectively shortens the arrival time of the patient for the first-onset AIS patient, and has important public health significance and clinical value.

The invention relates to the technical field of skin ulcers, and discloses a skin ulcer prognosis prediction method and system based on a column diagram, and the method comprises the steps: obtaining a clinical feature data set of a patient; preprocessing the clinical feature data set to obtain a preprocessed clinical feature data set; inputting the preprocessed clinical feature data set into a pre-trained ulcer prognosis prediction model to obtain weight coefficients of independent risk factors; constructing a visual scoring system according to the weight coefficient; and according to a scoring result in the visual scoring system, predicting an ulcer prognosis poor probability. According to the skin ulcer prognosis prediction method and system based on the column diagram, the independent risk factors influencing ulcer prognosis are explored, and the prediction model is established through the regression curve and the column diagram, so that a patient with poor ulcer prognosis is identified, a more reasonable treatment scheme is established in the early stage of a disease, and the healing time of the ulcer is shortened.

The application provides an improved FMEA system based on MCDM and Catboost, which has the characteristics that it includes l expert ends and an FMEA end, wherein the FMEA end includes a degree calculation module for obtaining the center degree and the cause degree corresponding to N failure modes respectively; a key failure mode screening module for screening n key failure modes from the N failure modes according to the center degree and the cause degree; a weight calculation module for calculating the risk factor comprehensive weight and the expert comprehensive weight; a comprehensive score calculation module for calculating the comprehensive score of each key failure mode; a feature calculation module for calculating the characteristic value and the closeness degree of the cloud model of each key failure mode; and a risk level analysis module for obtaining the risk level corresponding to the n key failure modes. In summary, the method can select key failure modes from all failure modes and obtain relatively accurate risk levels.

The application provides a model construction and verification method and nomogram for predicting the clinical prognosis of SFTS patients complicated with AP. By collecting the clinical data of SFTS patients, the independent risk factors related to adverse prognosis are screened out by using LASSO regression analysis, a multi-factor Logistic regression model is constructed, and the model is visualized by nomogram for predicting the adverse prognosis of SFTS patients complicated with AP. The model has high discrimination and calibration, and can provide an effective prediction tool for clinicians to optimize clinical decision-making.

This invention relates to the field of information platform construction technology, and more particularly to a method and system for constructing a sports information intelligent service platform. The method includes the following steps: collecting user motion image data and user basic health data; extracting key body node position information during the user's movement; generating a user movement trajectory curve based on the key node position information, and calculating the deviation angle and distance from a standard trajectory template to obtain movement correctness data; constructing a user fitness risk assessment model based on the movement correctness data and user basic health data, and analyzing the difference from a preset safety threshold to generate tiered health guidance data; decomposing the tiered health guidance data into quantifiable progress units to generate progress tracking data. This invention, by combining user motion image data and basic health data, constructs an assessment model capable of identifying individualized risk factors, achieving more accurate safety guidance.

This invention belongs to the field of medical device technology, specifically disclosing a method for predicting the risk of coronary artery disease in patients with chest pain and a daily exercise device. The method includes the following steps: collecting clinical data from multiple samples; performing statistical analysis on the clinical data to obtain risk factors; constructing a prediction model, inputting the risk factors into the prediction model, and training the prediction model; obtaining risk factors from the electronic medical record data of chest pain patients and inputting them into the trained prediction model to predict whether the chest pain patients have coronary artery disease. This technical solution, by identifying key clinical features and establishing a prediction model, can help to identify high-risk patients in a timely manner, thereby providing a basis for early intervention and improved prognosis, and has significant clinical application value.

The invention discloses a postoperative shoulder pain occurrence risk prediction method. The method comprises the following steps: S1, obtaining risk factors of postoperative shoulder pain of each patient sample; s2, using a GA algorithm to process the data set I by taking maximization of AUC as a target, and searching an optimal variable combination; s3, obtaining each core sub-factor; s4, outputting a column graph model; s5, substituting the data set II into the column graph model, calculating the predicted occurrence rate of each shoulder pain, and then calculating the AUC2 and the calibration curve of the column graph model in the data set II; when the AUC2 is not lower than the preset threshold value and the calibration curve meets the calibration slope range, the column graph model is used for postoperative shoulder pain prediction of the new patient. According to the method, the risk factors are integrated, the genetic algorithm is used for intelligent feature screening, the prediction model with high distinction degree and calibration degree is constructed, a strict data set verification and loop optimization mechanism is adopted, it is ensured that the model is stable and reliable, and finally risk visualization is achieved through a visual column graph.

The invention discloses a traditional Chinese medicinal material coupling heavy metal environment risk and health risk comprehensive assessment method, and particularly relates to the technical field of environment health risk assessment. Collecting soil and water samples, types and concentrations of heavy metal elements, soil physical and chemical parameters and medicinal material variety information of a traditional Chinese medicinal material planting area; constructing a migration enrichment model; estimating the content of heavy metals in the medicinal material by combining the enrichment coefficient of the traditional Chinese medicinal material variety; collecting population intake path data, constructing a multi-path exposure model, and calculating individual daily average intake dose; establishing a non-carcinogenic risk index and carcinogenic risk probability model based on the toxic equivalent and the intake dose; and finally, constructing a multi-factor risk assessment model fusing the content estimation, the exposure level and the health risk, and outputting a spatial distribution map layer and a key risk factor sorting table. According to the method, systematic and quantitative evaluation of the pollution risk of the traditional Chinese medicinal materials from an environment source to a health end point is realized, and the method has the advantages of high precision, strong adaptability, strong visual expression ability and the like.

The invention provides a public health parallel trend data matching method and system based on a clustering algorithm. The method comprises the following steps: collecting health result data and baseline health risk factor data of a target area experiment group and a control group; processing the baseline health risk factor data by adopting a clustering algorithm to obtain a clustering result, and performing parallel trend matching based on the clustering result and the health result data to obtain a region matching result; and outputting matching pair list data of the target region based on a region matching result. Through a multi-dimensional covariable clustering matching method, the problem of covariable imbalance caused by the fact that a multi-dimensional covariable is reduced into a one-dimensional tendency score by a traditional tendency score model is solved, a result variable time sequence similarity measurement criterion is constructed, and the result variable trend consistency of an experimental group and a control group is synchronously optimized in the matching process.