52 results about "Baseline characteristics" patented technology

The embodiment of the invention provides a multi-modal data fused postoperative plastic surgery infection early warning method and system. The method comprises the following steps: firstly, performing quality evaluation and restoration on postoperative wound images, clinical texts, wearable vital signs and baseline features; performing double-flow coding and priori segmentation on the repaired image to obtain a coarse mask; constructing a super-pixel graph, obtaining a topological consistent fine mask through a graph neural network, and quantifying the erythema / exudation area; the text and the vital signs are coded respectively and then fused with the image features and the masks through cross-attention and self-attention, and a fusion implicit vector and uncertainty are obtained through quality weight gating; an individualized dynamic threshold value is constructed by combining baseline risk, logarithmic probability calibration and a sliding window trend, a confidence band is output by using conformal prediction, and early reliable infection early warning is realized.

The invention provides an atrial fibrillation postoperative recurrence prediction method fusing electrocardiosignals and clinical characteristics. The method comprises the following steps: acquiring data of a patient before an ablation operation, and carrying out resampling, denoising and normalization preprocessing and data segment segmentation on an electrocardiosignal; extracting spatio-temporal features by using a deep network containing a residual convolutional block and a long and short term memory module; screening high-discrimination clinical baseline features through statistical analysis and a machine learning model; extracting time-frequency domain and nonlinear features of short-time heart rate variability; designing a cross-modal attention fusion module to carry out feature adaptive weighted fusion; and outputting a recurrence probability through a multi-layer perceptron based on the fusion features. The method improves the prediction precision through feature complementarity, facilitates the recognition of high-recurrence-risk patients, is suitable for sinus heart rhythm signals or atrial flutter and atrial fibrillation signals, and has a certain application value in the field of cardiovascular precision medical treatment. The method can be popularized to all prediction researches based on the electrophysiological signals.

The invention discloses a CT image change-based lung cancer curative effect dynamic prediction method and system, and relates to the technical field of medical imaging. Clinical information, a baseline CT image and a follow-up CT image of a patient are acquired; performing feature extraction on the baseline CT image and the follow-up CT image to obtain baseline features and follow-up features; obtaining an enhanced CT dynamic feature matrix and a flat-scanning CT dynamic feature matrix based on the baseline features and the follow-up visit features; inputting the enhanced CT dynamic feature matrix, the plain-scan CT dynamic feature matrix and clinical information into a curative effect prediction model, outputting logic risk scores at different time points, and obtaining progress or death risk probabilities of the patient at different time points; according to the method, image feature changes in a baseline and follow-up visit are effectively represented through a dynamic feature matrix, correlation between the interior of the dynamic feature matrix and between different matrixes is identified through a Transform attention network to predict the curative effect, and the relationship between accumulated data at each follow-up visit time point and the curative effect is learned through a time-sharing training strategy.

The invention relates to the technical field of biological and medical diagnosis, in particular to a biomarker for predicting whether a patient suffering from pyotoxic myocardial injury has a short-term death risk or not and application of the biomarker, and the biomarker is the ratio of erythrocyte distribution width to albumin. According to the method, retrospective queue research design is adopted, two data sets, namely MIMIC-IV and eICU-CRD, are used, suppurative myocardial injury patients meeting conditions are included, covariants such as baseline features, laboratory inspection results and complication information of the patients are collected, the patients are divided into two groups according to RAR medians, the relation between RAR and short-term death of the patients is analyzed by adopting multiple statistical methods, and the accuracy of the short-term death of the patients is improved. A risk prediction model of short-term death of the patient suffering from the pyotoxic myocardial injury is constructed, so that a clinician can identify the patient suffering from the pyotoxic myocardial injury with high death risk in an early stage, a personalized treatment strategy can be formulated more accurately, the survival rate of the patient suffering from the pyotoxic myocardial injury is increased, and the method has a wide application prospect.

The invention discloses a depression recurrence risk intelligent early warning method, system and device based on multi-modal data and artificial intelligence, and relates to the field of depression classification early warning, and the method comprises the steps: carrying out the individualized deviation calculation based on the baseline features and multi-modal brain image features of a target patient, obtaining an individual multi-modal brain image deviation feature vector; dimension reduction processing is carried out on the individualized multi-mode brain image deviation feature vector, the genetic features and the environment and clinical features, the individualized multi-mode brain image deviation feature vector, the genetic features and the environment and clinical features are input into a pre-trained layered integrated classification model and a pre-trained layered integrated risk early warning model, and depression subtype classification tags and risk probabilities are obtained; in the pre-training process of the hierarchical integration classification model and the hierarchical integration risk early warning model, multi-modal feature fusion and hierarchical integration learning strategies are adopted, and samples from a plurality of data centers are used for model training and verification. According to the method, the accuracy and individualization degree of classification and early warning are improved, and the generalization ability and robustness of the model are also improved.

The invention discloses a fault diagnosis method and system for hydraulic element general equipment, and relates to the technical field of hydraulic element fault diagnos.The method comprises the steps that control channels of the general equipment are recognized based on hydraulic elements, transient signals are collected in all the control channels in real time, preset interference constraints are introduced, and fault diagnosis is conducted on the general equipment; generating an interference factor through a high-entropy random disturbance source, performing disturbance analysis on the amplitude, the frequency and the waveform state of the transient signal, and generating an interference identifier; a health state vector is formed in combination with the operation working state, the deviation degree of the health state vector and the health baseline characteristic spectrum is compared, if the deviation degree is smaller than a standard deviation threshold value, a state evaluation strategy is executed in the first diagnosis channel, and a health stability trend is recognized; if the deviation degree is greater than or equal to the standard deviation threshold value, executing a fault diagnosis strategy in the second diagnosis channel, and identifying a fault sign; according to the invention, efficient division of labor of health assessment and fault diagnosis is realized, and the effect of accurately identifying fault signs is achieved.

The invention belongs to the technical field of medical data processing, and discloses a craniocerebral injury delayed deterioration prediction method and a related device, and the method comprises the steps: obtaining clinical data of a to-be-predicted patient; the clinical data of the to-be-predicted patient comprises basic clinical data, medical image data and laboratory examination data of the to-be-predicted patient; preprocessing the clinical data of the to-be-predicted patient to obtain clinical baseline features of the to-be-predicted patient; inputting the clinical baseline features of the patient to be predicted into the craniocerebral injury delayed deterioration prediction model to obtain a craniocerebral injury delayed deterioration prediction result of the patient to be predicted; the craniocerebral injury delayed deterioration prediction model is a dichotomy machine learning model under a predetermined outcome event, and the predetermined outcome event is determined according to a preset quantitative index of a craniocerebral ROI region; according to the method, the accuracy of the prediction result of the delayed deterioration of the craniocerebral injury is remarkably improved, the secondary injury risk of a patient with the craniocerebral injury is more accurately predicted, and an effective prognosis evaluation tool is provided for clinic.

The invention provides an ultrasonic evaluation system and method for heart segmental motion abnormality, and relates to the technical field of artificial intelligence, and the method comprises the steps: collecting a continuous multi-frame echocardiogram of a patient; carrying out heart structure segmentation on each echocardiogram to obtain a plurality of heart segments, and respectively tracking and extracting motion features of each heart segment in a heart cycle to form a motion feature time sequence of each heart stage; and calculating a difference value between the motion feature time sequence and a baseline feature of a pre-constructed normal heart motion mode, and when the difference value exceeds a preset threshold value, outputting an evaluation result that the heart segment is in the abnormal motion mode. The method has the advantages that diagnosis efficiency is improved, workload of doctors is relieved, personal errors are reduced, accuracy, consistency and objectivity of diagnosis are improved, final evaluation results can provide effective reference for the doctors, diagnosis ability of primary doctors is improved, and dependence on experience of the doctors is reduced.

The invention relates to the technical field of medical data analysis, in particular to a hepatic fibrosis outcome prediction and intervention recommendation method based on dynamic data, which comprises the following steps: collecting biological samples and clinical index data from a target individual at a plurality of continuous time points, and constructing a multi-modal time sequence data set; processing the data set to extract dynamic features reflecting a change process, wherein the dynamic features comprise trend features and event features; inputting the baseline features and the dynamic features of the target individual into a pre-trained hepatic fibrosis outcome prediction model, and outputting hepatic fibrosis index prediction values at a plurality of time points in the future to form a prediction outcome trajectory; when the trajectory meets an early warning condition, matching candidate donors from a pre-constructed donor feature library and generating an intervention recommendation list based on the current state and trajectory features of the target individual; and when new time point data is obtained, updating the data set and repeating the process. According to the method, dynamic and quantitative prediction of the hepatic fibrosis outcome trajectory is realized, and data-driven personalized intervention recommendation is provided.

The application provides a multi-parameter fusion real-time monitoring and early warning method and system for anesthesia depth, relates to the technical field of medical monitoring, and comprises the following steps: acquiring physiological parameter data, performing multi-scale decomposition and calculating sample entropy values, extracting baseline features and fluctuation features to form an anesthesia feature vector; an initial anesthesia depth index is output by using a deep learning model; a cause-effect correlation matrix is constructed by calculating time-varying transfer entropy based on intrinsic mode function decomposition, dominant cause-effect patterns are extracted, and the anesthesia depth index is corrected; and real-time monitoring and early warning reports are generated. The application can improve anesthesia depth evaluation accuracy and reduce anesthesia risks.

This invention provides a method for predicting individual mortality rates based on activity data from wearable devices, comprising the following steps: acquiring multi-source characteristic data of the target individual, wherein the multi-source characteristic data includes at least activity data and baseline characteristic data, the activity data being collected by the wearable device worn by the target individual over a continuous monitoring period, and the baseline characteristic data including at least one of demographic characteristics, health characteristics, living environment characteristics, and lifestyle characteristics; processing the activity data to calculate an average activity data feature value representing the overall activity level of the target individual; inputting the average activity data feature value and the baseline characteristic data together into a trained nonlinear machine learning prediction model, and the prediction model outputting a predicted mortality risk value for the target individual within a preset future time window. This invention integrates the average activity data feature value with traditional baseline characteristic data, enabling risk assessment to be updated synchronously with an individual's daily activity level.

The application discloses a female stress urinary incontinence acupuncture treatment optimization method based on multi-modal data fusion, and belongs to the technical field of intelligent auxiliary medical treatment. The method comprises the following steps: through a three-level representation learning subnetwork comprising modal difference extraction, cross-modal attention alignment and fusion feature enhancement, unified representation learning is performed on multi-source data such as patient baseline features, pelvic floor image sequences, electromyographic signals and curative effect indexes; based on the representation and treatment parameters, a dynamic time sequence kernel fusion method is used to predict the treatment state change trend of the patient at multiple future time points; finally, a state-scheme cross attention mechanism is introduced, and combined with the prediction result and historical treatment scheme features, an individualized acupuncture acupoint, manipulation strength and treatment cycle optimization suggestion is adaptively output. The application realizes an end-to-end intelligent diagnosis and treatment closed loop from data fusion, curative effect prediction to scheme recommendation, and improves the accuracy and clinical effect of acupuncture treatment.

The method of the embodiment of the application proposes a multi-modal data fusion postoperative infection early warning method and system. First, the postoperative wound image, clinical text, wearable vital signs and baseline features are quality evaluated and repaired. The repaired image is double-flow encoded and the coarse mask is obtained by prior segmentation. The superpixel graph is constructed and the topologically consistent fine mask is obtained by the graph neural network to quantify the erythema / effusion area. The text and vital signs are encoded and fused with the image features and the mask through cross-attention and self-attention, and the fused hidden vector and uncertainty are obtained through the quality weight gate. The individualized dynamic threshold is constructed by combining the baseline risk, the logit calibration and the sliding window trend, the shape-preserving prediction output confidence band is used to realize the early and reliable infection early warning.

The invention belongs to the technical field of data mining, and particularly relates to a method for predicting AD conversion probability by an attention-based deep learning method, which comprises the following steps: acquiring various examination data of a patient to be predicted, and dividing the data into follow-up data and baseline data according to follow-up features and baseline features; pre-processing follow-up data, and inputting the pre-processed follow-up data into the constructed base model to obtain follow-up data representation; and inputting the follow-up data representation and the baseline data into a final decision maker to obtain the probability that the next access of the to-be-predicted patient is converted from MCI to AD. On the basis of the principle of an attention mechanism, an attention variant form is designed for a prediction scene of current time sequence data, and weights are given to different features and time points, so that the relationship between the features and the time points is captured, and the importance of the different features and the time points is determined.

The invention discloses a pulmonary embolism risk prediction model for a critical community-acquired pneumonia patient and an establishment method. The method comprises the following steps: 1, inputting patient data; 2, sorting and cleaning data, especially processing missing values and abnormal values; 3, randomly splitting the data into a training set and a verification set in proportion, and performing population and baseline feature relationship analysis on the split data set; 4, the LASSO regression and the multi-factor logic regression are adopted to determine the independent risk factors of the intensive care unit-intensive community acquired pneumonia, the ICU-SCAP patient pulmonary embolism and the PE, and a risk prediction model is constructed on the basis of the training set; and 5, performing internal verification on the risk prediction model based on the verification set, and evaluating the distinction degree, the calibration degree and the clinical benefit of the risk prediction model. Based on the data processing strategy of the system, in combination with advanced modeling methods such as LASSO, GAM, RCS and the like, the PE risk of the ICU-SCAP patient is accurately evaluated, and the method has good technical advancement, stability and clinical practical value.

The invention relates to a medical assistance method and system based on baseline features and machine learning. The method comprises the following steps: performing data acquisition and feature representation to construct baseline features expressed by full information and a binary input vector first part corresponding to the baseline features; determining advantage features and disadvantage features; calculating a dominant distance between the baseline feature and the dominant feature; calculating a disadvantage distance between the baseline feature and the disadvantage feature; respectively taking the dominant distance and the inferior distance as a second part and a third part of an input vector; and constructing a classification model, and inputting the input vector into the classification model to obtain a classification result. According to the method, multiple baseline factors before mobilization are comprehensively considered, prediction classification adjustment of forward and reverse evidence crossing is performed based on different use stages of the model, and a more scientific and accurate decision basis is provided for mobilization treatment.

To provide techniques for predicting the risk of a subject experiencing a cytokine release syndrome of at least a threshold grade subsequent to receiving a treatment.SOLUTION: A risk may be predicted based on, for example, a set of baseline characteristics, a risk-score generation model, an on-treatment cytokine level, and / or a treatment dosage. The risk may be used to generate an output corresponding to a recommendation as to whether to monitor the subject via in-patient monitoring.SELECTED DRAWING: Figure 1

The invention discloses a high-risk chest pain prediction analysis method and device based on machine learning and a readable medium, and relates to the field of data analysis, and the method comprises the steps: carrying out the data enhancement and denoising of a data set through employing an SMOTE-ENN; screening baseline features in the processed data set by using a sequence forward selection method based on feature importance of a tree model, and verifying the validity of the screened features by using a Boruta algorithm to obtain important features; acquiring values of important features of a to-be-predicted chest pain patient, and inputting the values into the trained chest pain risk layering prediction model to obtain a corresponding chest pain risk layering result; and determining the maximum value of troponin in the important features as the feature influencing the prediction result of the chest pain risk hierarchical prediction model through SHAP analysis, analyzing the relationship between the maximum value of troponin and the death rate in the hospital, and determining the numerical value of the maximum value of troponin corresponding to the maximum death rate in the hospital. The problem of low accuracy of chest pain risk layered prediction is solved.

The invention discloses a blood fat-clinical outcome correlation analysis system and decision-making method oriented to an assisted reproduction and pregnancy period, and belongs to the field of medical information technology and intelligent diagnosis and treatment, and the method comprises the following steps: obtaining blood fat data and clinical baseline characteristics of a patient on an ovulation promotion starting day and a trigger day; adjusting a preset standard metabolic response curve by taking the clinical baseline characteristics as adjusting parameters based on blood fat data, and performing constraint fitting on the adjusted curve through parameter inversion to generate a personalized metabolic response curve and extract dynamic response characteristics; inputting the dynamic response features and the clinical baseline features into a risk-outcome prediction model to obtain a prediction result; and generating a clinical decision suggestion according to the prediction result and the decision rule. According to the method, discrete blood fat data are constructed into a continuous personalized metabolic response curve by adopting a method based on parameter inversion and constraint fitting, and dynamic response characteristics are extracted from the continuous personalized metabolic response curve for outcome prediction, so that the clinical outcome can be predicted more accurately, and a reliable basis is provided for personalized clinical decision making.

The application belongs to the technical field of medical information processing, and discloses an emergency patient condition dynamic evaluation method based on a mobile terminal. A patient identification, a priority weight and a condition baseline feature vector are encapsulated in a queuing two-dimensional code by acquiring patient triage results and baseline sign data. Queuing information and the baseline feature vector are obtained by scanning the code with a mobile terminal, and qualitative clinical observation information collected by nurses is converted into a quantitative offset value based on a clinical observation semantic knowledge base. The quantitative offset value and the baseline feature vector are subjected to offset amount operation, and a condition deterioration risk index is calculated in combination with queuing duration, triage level and queuing environment. The just-visit priority weight is dynamically adjusted according to the condition deterioration risk index, and a queuing sequence is reconstructed, which is reordered in an emergency information system and a change notification is pushed to related patients and clinics. The application provides high-quality technical support for emergency queuing management and intelligent triage.

The application provides a CAR-T cell therapy toxicity management optimization method and system, relates to the technical field of artificial intelligence, and comprises the following steps: acquiring clinical data of a blood tumor patient receiving a CAR-T cell therapy; performing missing value interpolation and standardization processing on baseline characteristic information and process monitoring data to obtain a standardized data set; obtaining a calibrated virtual patient physiological model according to a preset tumor cell kinetics model, an immune cell kinetics model and a toxicity reaction model; taking the calibrated virtual patient physiological model as an interactive environment to obtain a correlation database; constructing a deep Q network intelligent agent with a maximum reward function as a target to obtain an optimized deep Q network intelligent agent; and inputting the process monitoring data into the optimized deep Q network intelligent agent to obtain a target strategy, so that the individualized CAR-T treatment strategy balancing the therapeutic effect and toxicity can be automatically output by inputting the real-time monitoring data of the patient.

The invention provides a clinical test target information extraction method and device, computer equipment and a computer readable storage medium. The method comprises the steps of obtaining a clinical test text; inputting the clinical test text into a trained target information extraction model, and outputting target field information in the clinical test text; wherein the trained target information extraction model is obtained by training preset label contents and a medical knowledge graph, and the preset label contents comprise indications, stages, pathologies, biomarkers, baseline features, treatment threads and scheme elements; and if the target field information is valid field information, summarizing the valid field information to construct a medical database. By adopting the method, the extraction efficiency of the clinical test target information can be effectively improved.

The invention provides an OSA grading method and system based on multidimensional marker instant signal denoising and a physiological adversarial network, and relates to the technical field of medical instrument data processing and artificial intelligence auxiliary diagnosis. The method comprises the following steps: acquiring an original optical absorption signal of a multi-dimensional marker and a synchronous environment temperature and humidity sequence; inputting the data into a thermodynamic-optical compensation model guided by physical information to carry out bottom noise deduction and denoising to obtain high-fidelity marker quantitative data; physiologically decoupling the high-fidelity data and clinical baseline features, respectively mapping the high-fidelity data and clinical baseline features into a mechanical hypoxia feature group and an inflammatory barrier feature group, and inputting the mechanical hypoxia feature group and the inflammatory barrier feature group into a double-flow network; a pathological confrontation cross gating unit is used for simulating the human body confrontation process that negative pressure is sucked in the chest to break through an anti-reflux barrier, nonlinear game fusion is carried out, and a grading result of Obstructive Sleep Apnea (OSA) and the risk of concurrent chronic reflux cough are output. According to the method, the problem that the basic layer detection is easy to distort is solved, and the technical effects of high interpretability and accurate diagnosis of the OSA co-disease mechanism are realized.

The present invention relates to the technical field of intelligent diagnosis of acute ischemic stroke, and specifically to a method, device, equipment and medium for predicting the risk of hemorrhagic transformation. The method includes: obtaining magnetic resonance imaging data of the brain tissue of a target object, and clinical baseline data of the target object; determining imaging features of the brain tissue of the target object according to the magnetic resonance imaging data; determining clinical baseline features of the target object according to the clinical baseline data; and determining a prediction result of the risk of hemorrhagic transformation of the target object based on the imaging features and the clinical baseline features. The method for predicting the risk of hemorrhagic transformation of the present invention can improve the accuracy of HT risk prediction.

The application provides a drug data mining method, device and medium based on a large model, which comprises the following steps: obtaining clinical data of sample patients, wherein the sample patients comprise first patients receiving a target drug produced by a first manufacturer and second patients receiving a target drug produced by a second manufacturer; predicting a propensity score of the sample patients based on baseline characteristics of the sample patients, generating a first patient queue of the first manufacturer and a second patient queue of the second manufacturer based on the propensity score; performing drug data mining on the clinical data of the first patient queue and the second patient queue respectively to obtain drug data mining results of the first patient queue and the second patient queue; and generating a control mining result of the target drug produced by the first manufacturer based on the drug data mining results of the first patient queue and the second patient queue. The method, device and medium provided by the application guarantee the reliability and scientificity of the control analysis and improve the drug data mining efficiency.

The application provides a gynecological clinical path prediction method based on neuron level feature selection, and belongs to the technical field of path prediction, and comprises the following steps: dividing gynecological electronic medical record data of a patient into time sequence features and baseline features, inputting the time sequence features into a trained GRU network for feature coding, obtaining dynamic change rules of various clinical indexes over time, and converting the baseline features into feature vectors through an embedding layer; calculating feature importance based on the patient's last gynecological visit record and the visit scene, projecting the dynamic change rules of various clinical indexes over time and the feature vectors into a new representation space based on the feature importance to obtain a projection matrix to generate a gating vector, and screening and fusing the features of a main task layer and an auxiliary task layer respectively to obtain a final feature representation and obtain a multidimensional prediction result. More personalized and accurate diagnosis and treatment suggestions are provided for doctors.

This application relates to the field of medical data processing, proposing a medical data processing method, related apparatus, and storage medium. The method includes: acquiring electronic health records, genomic data, family medical history, lifestyle and behavioral data, and environmental exposure data of a target user within a preset time window; determining the target user's temporal characteristics, static baseline characteristics, and interaction characteristics; predicting the risk value of the target user for a target disease in multiple future time periods; determining the risk gradient value of the target user for the target disease in each future time period; and determining a health screening strategy for the target disease based on the risk value and risk gradient value of the target disease in multiple future time periods. This application predicts the future risk value and risk gradient value of the target disease using multimodal data of the target user, and formulates targeted health screening strategies, fully considering individual differences. The formulated health screening strategies are more tailored to each individual's actual situation.

The invention belongs to the technical field of medical data processing and intelligent decision support, and particularly relates to an insulin pump dose decision system, method and device, a medium and a program product, the system comprises a multi-source data processing unit which obtains patient baseline features and carries out risk stratification, and generates a risk suppression signal containing a nonlinear attenuation coefficient when the risk is high; the dual-channel dose prediction unit generates an initial recommended dose according to the baseline features and the risk signals; the simulation deduction unit receives the dose parameters, constructs a virtual metabolism model and generates blood glucose predicted values and interval lower limits at a plurality of preset time points; the safe closed-loop feedback control unit is connected with the simulation deduction unit and the double-channel dose prediction unit, corresponding feedback correction is triggered based on the risk state, accurate dose infusion is achieved, and clinical inertia and over-treatment risks are avoided.

This invention discloses a method and system for predicting the prognosis of myocardial function in sepsis based on imidazole propionate targeted intervention. The method includes: firstly, collecting baseline characteristic data of sepsis patients and dynamic response data after imidazole propionate targeted intervention, and simultaneously recording time parameters from sepsis diagnosis to intervention initiation. Then, transforming the data according to preset standardization rules to obtain metabolic characteristics, myocardial function comprehensive data, mitochondrial function comprehensive data, and intervention response parameters, filtering out abnormal data and retaining valid data. Subsequently, the standardized data are substituted into a multi-parameter nonlinear weighted model fusing exponential and logistic regression functions to simultaneously calculate myocardial function recovery assessment values ​​and adverse prognostic risk values. Finally, based on the assessment results, prognostic grade intervals are defined, and differentiated intervention plans are formulated to complete the closed-loop treatment optimization of "prediction-intervention-dynamic reassessment." This improves the targeting and effectiveness of clinical diagnosis and treatment.

The application relates to a jaw and facial bone fracture internal fixation plate selection decision method and system, which belongs to the technical field of medical artificial intelligence and relies on a pre-trained degradable internal fixation material prediction model and a titanium internal fixation material prediction model. The two kinds of implant materials can be simultaneously quantitatively evaluated based on the same preoperative characteristic data of a patient, the risk levels of the two kinds of materials are divided by corresponding threshold values, and then individualized material selection suggestions are output by combining double risk level linkage determination rules. Unlike the traditional selection method which only relies on the subjective judgment of the clinical experience of doctors, the method realizes the quantitative correlation between the baseline characteristics of the patient and the prognosis risk of the material, avoids the defect that a single group comparison conclusion cannot adapt to individualized diagnosis and treatment, quantitatively evaluates the objective and repeatable risk assessment results, effectively improves the scientificity and precision of the selection of the internal fixation material for the jaw and facial bone fracture, and reduces the probability of selecting an unsuitable implant material for a patient with a high complication risk.