205 results about "Risk factor" patented technology

The invention relates to a food safety risk analysis method and system based on big data. The method comprises the steps of collecting food full-life-cycle key data through Internet of Things equipment and a data platform, and completing data format standardization to form a structured data set; key risk factors are extracted through the structured data set, a risk factor weight matrix is constructed, and a primary risk value of the food sample is calculated; establishing a time sequence prediction model by using the primary risk value and historical time sequence data, calculating a risk change trend, and predicting a future risk fluctuation condition; calculating a food risk grade according to the primary risk value and the risk change trend, and generating a corresponding risk classification label; performing comparative analysis on the risk classification label and an actual supervision result, calculating a model deviation, and optimizing a risk calculation and trend prediction model based on a deviation result; the optimized analysis results are integrated, the risk level and the change trend are displayed through a graphical interface, and intelligent early warning prompts and intervention decision suggestions are provided.

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 relates to the technical field of information platform construction, in particular to a construction method and system of a sports information intelligent service platform. The method comprises the following steps: collecting user motion image data and user health basic data; key body node position information in the movement process of the user is extracted; generating a user motion track curve based on the key node position information, and calculating a deviation angle and a deviation distance with a standard track template to obtain motion correctness data; constructing a user fitness risk assessment model according to the action correctness data and the user health basic data, analyzing the difference degree with a preset safety threshold, and generating layered health guidance data; and decomposing the layered health guidance data into quantifiable progress units, and generating progress tracking data. According to the method, the user motion image data and the health basic data are combined, the evaluation model capable of identifying individualized risk factors is constructed, and more accurate safety guidance is realized.

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 constipation prediction system based on an artificial intelligence large model, and relates to the technical field of medical health information. In order to solve the problems that complex risk factors of constipation are difficult to comprehensively capture, so that the feature coverage range of a prediction model is limited, group classification and risk level dynamic evaluation are not performed on users, so that an intervention scheme is high in universality, but accurate management requirements of different groups are difficult to meet. Multi-source data such as electronic medical records and physiological parameters are integrated through the multi-modal data processing module, refined classification of user groups is achieved, feature causal relationships are analyzed in combination with a knowledge graph technology, constipation prediction accuracy is improved, the intelligent prediction module dynamically adapts to a model according to group risk levels, and constipation prediction accuracy is improved. The probability distribution is output, a visual causal path report is generated, and the dynamic decision-making module adjusts an intervention scheme through doctor-patient cooperation based on a knowledge graph matching strategy set and optimizes a strategy in real time by using an effect feedback mechanism, so that personalized health management is realized.

The invention relates to a heart failure home patient-oriented multivariable trend anomaly detection method, which comprises the following steps of: constructing an initial contour of a multivariable health trend for a patient, and introducing a trend inertia vector: updating trend inertia to form an individual trend trajectory relationship; detecting an inertia breaking point in the trend trajectory diagram; analyzing whether the variables within 12-36 hours before and after the focusing breaking point analysis generate collaborative disturbance or not; performing reprocessing through a disturbance amplification operator to form a potential early warning factor; constructing a multivariable intervention graph by using the disturbance cooperation matrix; monitoring the offset direction and strength of the causal propagation chain on each path; if the plurality of paths are subjected to direction deviation in the same period at the same time, judging that the deviation is pathological trend deviation; converting the causal offset path into a single trend risk factor, spreading a plurality of weak signals in a variable graph, evaluating systematic influence, forming a fuzzy risk scoring curved surface relationship, and outputting an individual trend risk level; the sensitivity and continuity of trend identification are improved, individual differences are adapted, and the false alarm rate is reduced.

The invention relates to the technical field of model construction, in particular to a construction method of a disease feature recognition and evaluation model based on the Internet of Things. The method comprises the following steps that multi-modal physiological data of a patient are collected in real time through Internet of Things medical equipment, data preprocessing is conducted on the multi-modal physiological data of the patient, standard patient physiological data are generated, and the standard patient physiological data are stored in a distributed database; extracting gene sequencing data based on the biological sample library; performing global health risk factor perception on the standard patient physiological data to generate user level health risk perception data; and carrying out feature fusion on the user level health hazard perception data and the gene sequencing data to construct a disease feature matrix containing a space-time dimension. Through multi-modal data fusion, spatio-temporal evolution modeling, deep learning architecture and personalized risk assessment, the precision and timeliness of disease feature recognition assessment model construction are 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 present invention proposes an intelligent management system for chronic comorbidities in the elderly, including a comprehensive elderly assessment module, a multidisciplinary collaborative diagnosis and treatment module, a multiple medication risk prediction management module, a chronic disease intelligent follow-up management module, and a health science knowledge base module. The comprehensive elderly assessment module inputs the patient's individual chronic disease profile into a risk factor prediction model for prediction and outputs a comprehensive assessment result. The multidisciplinary collaborative diagnosis and treatment module is used by the first-visit / admitting physician to coordinate multidisciplinary physicians for collaborative diagnosis and treatment. The multiple medication risk prediction management module is used to provide patients with comprehensive diagnosis, multiple medication management, and risk prediction through the collaborative diagnosis and treatment. The chronic disease intelligent follow-up management module performs follow-up tracking, health risk prediction, and review prompts for patients. The health science knowledge base module matches and pushes corresponding health education knowledge. The present invention improves the accuracy and timeliness of risk assessment, prediction, and management of chronic comorbidities in the elderly, and plays a good auxiliary role in improving the quality of life of patients.

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 present invention relates to the technical field of medical information management for the elderly endocrine system, and specifically relates to a method for risk assessment of elderly endocrine diseases based on big data. The present invention screens out all the highest similarity vectors of the target medical vector; obtains the basic risk factors according to the detection time distribution and similarity degree between the two; obtains the self-risk factors by combining the detection times of all medical vectors; obtains the data change risk factors according to the number of similar patients with the disease and the data difference and detection time difference between the medical vector and the first medical vector; obtains the endocrine system stability factor by using the similarity degree between medical vectors, combines them to obtain the dynamic risk factor, and further conducts a risk assessment of elderly endocrine diseases for the person to be evaluated. The present invention takes into account the physiological changes of the elderly population and the timeliness of risk factors, making the risk assessment of elderly endocrine diseases more accurate.

The invention discloses a one-key intelligent filling control system and method based on a digital twin model, and the method comprises the following steps: S1, collecting filling data in real time, and constructing a standardized input data set; s2, constructing a multi-scale digital twinborn model, and outputting simulation state data; s3, acquiring a response sensitivity matrix and a risk factor matrix; s4, introducing a cuckoo search algorithm, calculating a disturbance weight according to the response sensitivity matrix, and initializing a control strategy population; s5, adjusting the jump direction and step length of the Levy flight by using the risk factor matrix; s6, calculating a fitness value; s7, deleting the control strategy which is marked to be failed according to a historical strategy, and performing disturbance adjustment on a scheme which is close to an efficient strategy; and S8, if the convergence condition is not met, repeatedly executing the steps S4 to S7, otherwise, outputting the control strategy with the maximum fitness value, and generating a one-key filling control instruction. According to the method, digital twinning and cuckoo search algorithms are fused, and intelligent control over the whole medicament filling process is achieved.

The invention discloses a cutting fluid system full life cycle management platform based on digital twinning, and relates to the technical field of cutting fluid life cycle management, a health score HQ and an abnormal factor psi are subjected to mathematical fusion to form a continuously changing risk intensity factor R, the limitation of risk judgment by a single score in the past is eliminated, and the safety of a cutting fluid system is improved. Therefore, the system can more carefully capture the risk evolution state in the running process of the cutting fluid, is particularly suitable for the scene that the treatment state continuously changes and the risk level gradually rises, and enhances the granularity and expression ability of risk assessment. The risk factor fusion process not only considers the macroscopic stability of the system reflected by the overall health score HQ, but also fuses the microscopic perturbation represented by the abnormal factor Psi, and realizes the two-dimensional modeling of the current state and the future trend, so that the system not only can evaluate the current risk level, but also can perceive the potential instability trend in advance, thereby improving the reliability of the system. And the risk perception capability of the platform on complex fluctuation working conditions is obviously enhanced.

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 gestational diabetes mellitus early risk accurate prediction method based on multi-feature fusion, and relates to the technical field of gestational diabetes mellitus risk prediction. The risk prediction efficiency is improved. Comprising the following steps: collecting clinical data of early pregnancy (8-16 weeks of pregnancy) through a multi-center electronic health record (EHR), cleaning the data according to a preset exclusion standard, dividing the data into a training set and a test set, and screening risk factors related to a GDM diagnosis standard based on literature retrieval; preprocessing the data, including missing value filling and abnormal value elimination, and determining an optimal feature subset through mutual information (MI), variance analysis (ANOVA) and incremental feature selection (IFS); through a multi-feature fusion method, multi-dimensional data such as demographic statistics, basic measurement, medical history, laboratory indexes and transcriptome genes are combined, and the prediction accuracy of the early risk of gestational diabetes mellitus is remarkably improved.

The invention provides a risk assessment method based on multi-factor combination. The method comprises the following steps: acquiring a plurality of risk factor parameters for assessing the operation risk of a target subject; based on a plurality of evaluation dimensions, respectively setting a weight distribution scheme under each dimension for the risk factor parameter; under each evaluation dimension, according to a corresponding weight distribution scheme, carrying out weighted calculation on the plurality of risk factor parameters to obtain a weighted value corresponding to each evaluation dimension; selecting a maximum value from the weighted values corresponding to the plurality of evaluation dimensions as a comprehensive score of the target subject; and determining a risk assessment result of the target subject according to the comprehensive score. Through multi-dimensional dynamic weight distribution and comprehensive scoring, comprehensive and accurate assessment of enterprise risks is realized, and supervision efficiency is effectively improved.

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 discloses an interpretable cardiovascular health assessment method and system based on multiple agents, and belongs to the technical field of biomedical engineering, and the method comprises the steps: processing original data to obtain a first data set, dividing the first data set, correspondingly inputting different agents, and enabling the agents to output corresponding risk prediction and confidence results; aggregating the risk prediction output by the intelligent agent to obtain a comprehensive risk score, and aggregating the weight value of the feature vector and the corresponding dominance to calculate a feature importance score; sorting the feature importance scores to obtain a dominant factor list, generating intervention priority scores according to the feature importance scores and the mutation scores, matching the dominant factors with corresponding suggestion entries, and outputting suggestions. According to the method, multi-source data are integrated, multiple agents are designed for cooperative reasoning, trend analysis is carried out on time evolution of key risk factors on the basis, a potential risk mutation window period is identified, and time reference is provided for early intervention.

The invention provides a risk prediction model for the death rate of senile sepsis patients in 28 days after the senile sepsis patients are transferred into ICU and a construction method of the risk prediction model, independent risk factors related to death occurrence in 28 days are determined through single-factor and multi-factor logistic regression analysis, a column chart is made, the prediction model of the patent is constructed, and the risk prediction model of the senile sepsis patients in 28 days can be used for predicting the death rate of the senile sepsis patients. A comparison result with a standard APACHE II score and a decline index (FI-lab) in recent years or an AUC of an ROC curve of other multi-index models shows that the prediction model provided by the invention is better in efficiency.

The invention provides a non-serious disease risk prediction method based on multi-dimensional health data analysis, and the method can collect the multi-dimensional health data of a user, builds a high-precision serious disease risk prediction model through multi-source data fusion and intelligent analysis in combination with a machine learning algorithm and a statistical model, and improves the risk prediction accuracy. The health state of the user is dynamically evaluated based on the deep learning model, the potential disease risk of the user is predicted by analyzing the relevance between historical data and future health risks, the health trend, risk factors and improvement suggestions are covered, the user is helped to make a scientific health management plan, the limitation of a traditional prediction method is broken through, and the user experience is improved. And real-time data updating and model optimization are supported, and a more accurate and personalized prediction result is provided.

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.