178 results about "Lasso regression" patented technology

The invention relates to a data processing method and system for multi-source complex biological information data. According to the method, expression profile data, gene variation data and clinical survival data are collected, and unified standardization processing is carried out on the collected data. Feature alignment is performed on different source data based on sample identifiers, a joint feature expression matrix is constructed, and a context dependency relationship across data types is maintained. On the basis, multi-stage feature screening is carried out through Lasso regression and information gain evaluation in sequence, and an optimal feature subset used for modeling is obtained. And further training a risk scoring model by adopting a Cox proportional risk regression method, and calculating a risk scoring value of the sample by utilizing the constructed continuous scoring function. And finally, dividing the score value into a plurality of risk levels, and generating a survival curve of each level in combination with a Kaplan-Meier estimation method so as to verify the risk layering effect and prediction significance of the model. According to the method, the accuracy of biological information modeling can be improved, and the method has good universality and practical value.

The invention relates to an advanced gastric cancer survival prediction system based on Lasso regression, Cox regression and an interpretable machine learning technology, and belongs to the technical field of medical artificial intelligence and intelligent decision support. According to the system, by collecting multi-modal clinical data (including demographic information, TNM staging, treatment modes, tumor grading and the like) of a patient, survival-related variables are screened by adopting Lasso regression and a Cox proportional risk model, and an optimized feature set is constructed. Based on the feature set, the system integrates various mainstream machine learning algorithms (such as XGBoost, Random Forest, SVM, Logistic regression and the like) to construct a prediction model, compares the performance of each model, and selects a model with an optimal effect as a main model. And hyper-parameter tuning is performed on the model through grid search and cross validation, so that the precision and generalization ability of the model are improved. An SHAP interpretability analysis method is introduced into the system, transparent interpretation is carried out on a model output result from the global level and the individual level, and the importance and directional effect of all variables in survival prediction are determined. Finally, the model is deployed on a terminal device, a doctor is supported to automatically output the survival probability and an explanation result after inputting patient information, and a reference basis is provided for clinical treatment decision and personalized management. The system has the advantages of high prediction precision, high interpretability, convenience in use, sustainable optimization and the like, is suitable for clinical aid decision-making scenes, and has good application prospects and popularization values.

The invention discloses a prostate cancer three-classification risk layering method based on an integrated learning model, and relates to the technical field of data processing and analysis. The method comprises the following steps: collecting clinical information and pathological data of a patient with increased PSA, and dividing the data into a training set and a test set; the training set is preprocessed, and prediction features are screened through LASSO regression; constructing a plurality of machine learning base models based on the features, and training and optimizing through cross validation; soft voting is constructed through an integration strategy, and an integration model is stacked; setting double thresholds according to the integrated model prediction probability, and establishing a layering rule; combining an integrated model and rules to form a three-classification model, and judging low, high and medium risks according to probabilities; and finally verifying the model diagnosis performance in the test set. According to the method, through cross-modal feature integration and ensemble learning, the method is PSAlt; accurate risk stratification is provided for 30 ng / mL people, biopsy decision-making efficiency is optimized, and excessive puncture and missed diagnosis risks are reduced.

The invention discloses a prone position ARDS patient death risk prediction system based on interpretable machine learning, which constructs an interpretable machine learning prediction model based on clinical data of an ARDS patient before the ARDS patient receives prone position ventilation treatment, and is used for identifying high-death-risk crowds in an ICU (Intensive Care Unit). According to the method, an optimal prediction model is determined by performing preprocessing and correlation and collinearity screening on original variables, selecting key features by adopting Lasso regression and combining multi-model training and cross validation. On the basis, an SHAP method is introduced to explain a model decision basis, a column graph is constructed, visual expression of a risk assessment result is realized, and clinical understandability and practicability of the model are improved, so that auxiliary support is provided for precise treatment and resource allocation.

The invention provides a gestational diabetes risk prediction system and method based on multi-modal data fusion, and the system comprises a data collection module which is used for integrating clinical indexes and medical record text data; the data preprocessing module converts the multimode data into numerical values and text variables which can be used for modeling; the variable screening module is used for extracting data features by adopting LASSO regression in combination with a recursive feature elimination algorithm and a Clinical-BERT model; the data prediction module is used for constructing a GDM risk prediction model through a dual-channel calculation unit and a fusion unit, generating an accurate risk probability and providing an interpretable clinical index in combination with an SHAP value; and a prediction result is output through the output module in the forms of a dynamic column diagram, a webpage calculator and an API interface, so that clinical operation and application are facilitated. According to the method, the limitation of a traditional prediction method is broken through, the prediction precision and the real-time monitoring capability are improved, and the development of precise medical treatment is promoted.

The invention discloses a COPD-FE risk prediction method based on disease and symptom combination, and is applied to the technical field of chronic obstructive pulmonary disease risk prediction. Comprising the following steps: acquiring chronic obstructive pulmonary frequent acute exacerbation influence factor data of a patient; the influence factors are screened through LASSO regression and an improved Boruta algorithm respectively; the LASSO independent influence factors and the Boruta independent influence factors are combined in different modes, a Logistic regression model and an XGBoost model are trained, and a plurality of COPD-FE risk prediction models are obtained; and evaluating the performance of all the COPD-FE risk prediction models, and selecting the COPD-FE risk prediction model meeting the requirement to predict the COPD-FE risk. According to the method, clinical data of patients are collected, a risk prediction model is constructed in combination with a feature selection method and machine learning, and an optimal model is screened out through comprehensive evaluation.

The invention discloses a hepatocellular carcinoma postoperative early recurrence prediction method based on multi-modal fusion. The method comprises the following steps: firstly, integrating clinical data of a training set, a preoperative enhanced CT image and a postoperative full-view digital pathological image, and carrying out standardized correction; then, traditional image omics features and deep learning features are extracted from the CT image, cell nucleus morphological features and tumor microenvironment spatial configuration features are extracted from the pathological image, and key feature signatures are screened out through a maximum correlation minimum redundancy algorithm (mRMR) and LASSO regression in combination with clinical features. And then carrying out progressive model construction by adopting an XGBoost algorithm, sequentially establishing a clinical single-mode model, an image single-mode model, a pathological single-mode model and a multi-mode fusion model, and explaining and visualizing the models by utilizing an SHAP value and a Grad-CAM technology. Finally, the performance of the model is evaluated in a multi-dimensional mode through internal cross validation, foresight and external independent validation, risk layering is carried out based on the prediction probability, and individualized postoperative management is guided.

The invention discloses a diagnosis method for predicting tuberculosis risk by using blood routine indexes, which comprises the following steps: (1) collecting open-source blood routine data, preprocessing, and dividing into a training set, a test set and a verification set; (2) screening blood routine examination and 25 indexes derived from the blood routine examination, and obtaining a preliminary screening result through LASSO regression analysis; (3) inputting the preliminary screening result into seven machine learning models, including a logistic regression model, a random forest model, a naive Bayes model, a K proximity model, a support vector machine model, an XGBoost model and a GBM model, for analysis to obtain a final variable combination and an optimal model; (4) inputting the verification set into the optimal model to obtain a DCA decision curve and a calibration curve; early discovery and precise diagnosis and treatment of tuberculosis are promoted, and meanwhile the pressure of medical resources is effectively relieved.

The invention provides a muscle fatty degeneration risk assessment method based on machine learning, and relates to the technical field of disease risk assessment. The method comprises the following steps: acquiring a data sample of an HD patient; carrying out feature screening on related features in the data sample according to a Boruta algorithm and Lasso regression based on interaction importance among the features, and generating an important feature set; based on the important feature set, constructing and training a plurality of machine learning prediction models by using the corresponding data samples, and determining an optimal model; inputting data of an actual HD patient into the optimal model to generate a prediction result; performing global explanation and local explanation on the prediction result based on the SHAP value; and generating a risk assessment result according to the global explanation and the local explanation of the prediction result. By adopting the method, the features can be accurately screened, and the prediction result of the model can be explained by accurately utilizing the SHAP value.

The invention discloses a method for predicting the curative effect of brain stimulation on MCI based on a multi-modal image, and belongs to the technical field of medical data process.The method comprises the steps that through a multi-modal MRI feature fusion model, structure and functional MRI indexes are synchronously integrated, feature selection and dimension reduction are conducted through comparison between independent sample t test groups and LASSO regression, and the curative effect of brain stimulation on MCI is predicted; compared with the existing method, the method has the advantages that the obvious contribution of the change of the collaborative structure image index and the functional image index to the predicted curative effect is verified through the structure-function coupling analysis, and the prediction model is constructed, so that the prediction efficiency is improved. The contribution direction and strength of each image feature to curative effect prediction are analyzed and quantified in combination with SHAP values, feature weights are automatically optimized according to SHAP value distribution, and model high-contribution feature brain regions are revealed through SHAP so as to clarify curative effect biomarkers.

The invention relates to the technical field of medical data analysis and clinical risk prediction, provides a construction method of a glucocorticoid induced diabetes risk prediction model based on an LASSO algorithm, and belongs to the technical scheme of data processing executed on computing equipment. Clinical data of inpatients receiving systemic glucocorticoid treatment are collected, key variables are screened through LASSO regression, and a Logistic regression model is constructed to achieve risk prediction. The model is composed of four conventional clinical indexes, has good distinction degree and calibration degree, and finally realizes individualized risk assessment through column diagram form output. The method is simple, practical and accurate, and is suitable for SDM prediction and intervention management of clinical high-risk groups.

The invention relates to the field of bioinformatics, in particular to a genome selection method, device and equipment, a medium and a computer program product. Integrating a plurality of machine learning models, including 13 algorithms such as a support vector machine (SVM), linear regression, Ridge regression, Lasso regression and the like; by combining automatic hyper-parameter optimization, parallel computing and efficient data preprocessing technologies, the breeding prediction precision and the computing efficiency are remarkably improved. The device comprises an IASML software module and an IASML cloud platform module, wherein software supports command line operation and is suitable for large-scale data calculation; the cloud platform provides a graphical interface and supports online data uploading, model selection, real-time monitoring and result downloading. According to the method, the problems of singleness, limited data scale, poor interactivity and the like of an existing tool model are solved, and an efficient, flexible and reproducible intelligent solution is provided for animal and plant breeding and genetic research.

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 embodiment of the invention provides a myocardial infarction risk prediction model training method and related equipment, and belongs to the technical field of machine learning. The myocardial infarction risk prediction model training method comprises the steps of obtaining initial training data, performing multiple times of characteristic variable screening on the initial training data through a single factor analysis method and an LASSO regression method to obtain training case data, and inputting the training case data into a BP neural network model for nonlinear data processing to obtain the prediction probability of myocardial infarction occurrence. And performing parameter optimization on the BP neural network model according to the prediction probability and the real occurrence probability to obtain a myocardial infarction risk prediction model. According to the embodiment of the invention, nonlinear processing can be carried out on high-dimensional or multivariable training case data through the BP neural network model, and the processing capability of complex data and the accuracy and stability of myocardial infarction risk prediction are improved.

The invention relates to a training method of a respiratory tract infection disease progress and prognosis prediction model. The training method comprises the following steps: extracting mRNA from peripheral blood of a target patient, and carrying out transcriptome sequencing to obtain a sequencing result; based on the ferroptosis related gene set, comparing ferroptosis score differences of two groups of patients with community-acquired pneumonia and sepsis, and screening corresponding ferroptosis related genes with statistical significance from a sequencing result; screening out genes meeting preset conditions from the ferroptosis related genes based on LASSO regression; and establishing an RTI clinical outcome prediction model through logistic regression by taking whether the patient is sepsis or not as an outcome dichotomy variable and taking the screened gene expression quantity as a prediction variable. According to the invention, after the prediction model is subjected to machine learning screening such as LASSO and the like, the core feature with the highest prediction value is reserved, so that the risk of over-fitting of the model on training data is reduced.

The invention discloses a carotid artery pulse wave velocity prediction method and device based on machine learning and a medium, and the method comprises the steps: carrying out the time domain feature analysis and correlation analysis of an original pulse wave data set of each test sample, and obtaining a plurality of key time domain features of each carotid artery position of each test sample; obtaining a total regression coefficient of the key time domain features of each carotid artery position by adopting an LASSO regression model; then, performing permutation and combination on the carotid artery positions to obtain a plurality of key time domain features under each carotid artery position combination of each test sample; constructing a test sample feature set of each carotid artery position combination, training a machine learning model according to the test sample feature set, and optimizing to obtain a carotid artery pulse wave velocity prediction model and a target carotid artery position combination; and performing carotid artery pulse wave velocity prediction on a to-be-predicted sample by using the carotid artery pulse wave velocity prediction model to obtain a predicted pulse wave velocity. The detection efficiency and the detection accuracy of the carotid artery pulse wave velocity are improved.

The invention belongs to the technical field of crucible deformation resistance prediction, and relates to a quartz crucible high-temperature deformation resistance rate prediction method based on machine learning. According to the method, key historical data are collected, and information such as aluminum alloy components, crucible physical attributes, using conditions and the deformation resistance rate is covered; through data preprocessing, the quality and consistency of a data set are ensured; through statistical methods such as Pearson's correlation coefficients, LASSO regression, principal component analysis and the like, features having significant influence on the anti-deformation rate are screened out, feature engineering is carried out to construct new interactive features, and the features can reflect the complex relation between the smelting conditions and the physical attributes of the crucible; the deep learning model is introduced, so that the prediction model can learn a deep mode in the data, and the prediction precision is remarkably improved; finally, the trained model can accurately predict the high-temperature deformation resistance rate of the quartz crucible according to real-time data, a scientific basis is provided for production decision making, and optimization of the production process and reduction of the cost are facilitated.

The invention provides a photoresist process parameter compensation method and a compensation control system, and the method comprises the steps: building a Lasso regression model through collected process parameters in a plurality of batches of photoresist processes, and carrying out the data training, the method comprises the following steps: reversely analyzing a nonlinear influence mechanism of an external environment temperature on the photoresist thickness through a model, then obtaining a compensation rule of a real-time environment temperature through a Lasso regression model, and dynamically adjusting a photoresist spin-coating rotating speed in a current batch photoresist process so as to maintain the thickness of the current batch photoresist to be constant; according to the method, the stability of the critical dimension (CD) of the photoetching process is improved, a photoetching process window is expanded (the exposure dose fluctuation range of a photoetching machine is widened and the debugging cost of the photoetching process is reduced), accurate temperature compensation of the photoetching process is realized, so that the no-load energy consumption of the photoetching machine is reduced, and the product yield and the reliability are improved.

The invention relates to the technical field of biological medicine, in particular to construction and application of a candida patient death risk prediction model. The model is based on traditional SOFA scoring, core variables are screened through single-factor and multi-factor logistic analysis and LASSO regression analysis, respiratory system, blood coagulation function and circulatory system SOFA scoring and key indexes such as the maximum lactic acid value, the minimum albumin value and the maximum blood urea nitrogen (BUN) are integrated, and a prediction model is constructed through logistic regression. Through multi-center data verification, the AUC of an internal verification queue reaches 0.826, the AUC of an external verification queue is 0.813, and a calibration curve shows that the predicted death rate is highly consistent with the actual death rate. Patients can be divided into a high risk group and a low risk group through a 13-score threshold value, and the 28-day survival rate difference is significant (plt; 0.05) of the method. An efficient and convenient early risk assessment tool is provided for critical candida patients, and clinical precise intervention and prognosis improvement are assisted.

The invention provides an underground water source water quality early warning method and system for environmental safety, and relates to the technical field of environmental protection and water quality safety, and the method comprises the steps: obtaining a plurality of water quality indexes of an underground water source in different historical periods and index values corresponding to the water quality indexes; screening the water quality indexes by combining a Lasso regression model and an FISTA algorithm, and determining key water quality indexes; constructing a two-dimensional tensor according to the key water quality index and the index value; constructing a water body pollution prediction model based on CNN and LSTM in combination with an attention mechanism; inputting the two-dimensional tensor into a water body pollution prediction model, and determining a pollution factor concentration; calculating the deviation between the pollution factor concentration and the pollution factor threshold value, and determining the water quality risk grade; and performing early warning on the water quality of the underground water source by combining an early warning grading strategy according to the water quality risk grade. The water quality safety of the underground water source can be ensured, and potential threats to the environment and human health are reduced.

The invention provides a method for constructing an ankylosing spondylitis ossification progress prediction model based on metabonomics and artificial intelligence. The method comprises the following steps: carrying out metabonomics analysis and peak extraction to obtain a characteristic ion peak table, carrying out compound identification on characteristic ion peaks, and carrying out characteristic screening through a lasso regression method, a multivariate statistical method of unbiased variable selection and a BORUTA method to obtain candidate characteristics related to the ankylosing spondylitis ossification progress; carrying out data standardization processing, principal component analysis and orthogonal partial least-partial-square discriminant analysis on the characteristic ion peak table, and carrying out pathway enrichment analysis to obtain differential metabolites; an LR model, an RF model and an SVM model used for distinguishing the ossification progress speed of the AS patient are established based on known risk factors, candidate features and differential metabolites, the efficiency of the three models is verified and compared, and finally the ankylosing spondylitis ossification progress prediction models with the optimal efficiency are obtained and serve as the LR model and the RF model.

The invention discloses a lung adenocarcinoma prognosis prediction model based on heme metabolism related genes and a construction method and application thereof. According to the invention, a heme metabolism related gene set is screened through a molecular characteristic database, and a gene significantly related to prognosis is screened by using transcriptome and clinical data of lung adenocarcinoma in a TCGA database and combining single-factor Cox regression, LASSO regression and multivariable Cox analysis. And through multi-time iterative modeling, selecting high-frequency stable genes and corresponding regression coefficient mean values to construct a heme metabolism risk score. According to the method, a random survival forest model is adopted to carry out iteration evaluation on gene importance for multiple times, the first 50% of genes which have the maximum influence on the model are screened, and finally the five important characteristic genes related to the heme metabolism risk are identified by taking an intersection with an LASSO result. The application provides new theoretical basis and technical support for precise treatment of lung adenocarcinoma, and has important clinical application value.

The invention discloses a regional interlocking cooperative early warning method, system and device, and a storage medium. The method specifically comprises the following steps: S1, obtaining field production data from a controller of each oil and gas production station; s2, performing dimension reduction extraction on the field production data of each oil and gas production station by adopting a principal component analysis method to obtain key data; s3, normalizing the key data, and then performing correlation degree analysis among the key data; s4, constructing a multivariate nonlinear regression and L1 regularization constraint model based on the correlation degree, and outputting a predicted value; and S5, determining a dynamic threshold value according to the segmented mean value and the standard deviation, and giving out early warning when more than 50% of predicted values exceed the dynamic threshold value. Main component analysis is carried out on a large amount of data of each station in a field area to extract key data, correlation analysis is utilized to obtain the correlation degree of the key data, the high precision characteristic of multivariate nonlinear regression and the characteristic of lasso regression parameter screening are combined, training and fitting are carried out by utilizing a neural network, and the accuracy of the data is improved. The prediction method with the minimum deviation and the optimal coincidence rate is obtained, when data with the high correlation degree changes at the same time, early warning is generated, and prediction is not affected by a single outlier.

The invention relates to a method and a system for discriminating years of raw Pu'er tea in different storage aging periods. The method comprises the following steps: S1, preparing a sample; s2, analyzing components to determine the content of non-volatile compounds; s31, dividing the sample into three storage aging periods; s32, screening the number and the type of the non-volatile compounds by adopting LASSO regression to obtain a key predictive factor; s33, taking the key predictive factor as an input variable, and combining linear discriminant analysis to construct a year discriminant model; s4, outputting a result; the method has the advantages that based on LASSO regression of non-volatile compounds, linear discriminant analysis and other frontier machine learning technologies, an accurate model for Pu'er raw tea year discrimination is constructed, a matched visual module is combined, an efficient and visual tool is provided for Pu'er raw tea year discrimination, and the Pu'er raw tea year discrimination method is suitable for popularization and application. And scientific technical support is provided for quality evaluation and market transaction of annual Pu'er raw tea.

The invention discloses a construction method and application of a neurological function prognosis prediction model after cardio-pulmonary resuscitation, and the method comprises the steps: obtaining clinical data of a patient reaching spontaneous circulation recovery after cardio-pulmonary resuscitation, and screening a clinical patient meeting a discharge standard; taking brain function classification as dependent variables, respectively adopting an LASSO regression analysis method and a Boruta feature screening method to screen feature variables, and selecting common feature variables; constructing a plurality of machine learning models based on the common feature variables, and drawing an ROC curve and a decision curve corresponding to each machine learning model; carrying out discrimination evaluation on the machine learning model by adopting AUC, evaluating clinical benefits of the machine learning model by adopting a decision curve, and screening an optimal prediction model; and explaining the optimal prediction model and the common feature variables by adopting an SHAP tool. The prediction model constructed by the invention can conveniently and quickly predict the prognosis condition of the neurological function within 24 hours after sudden cardiac arrest and resuscitation.

The invention relates to the technical field of electricity market day-ahead prediction, in particular to an electricity market day-ahead price difference prediction method and related equipment, and the method comprises the steps: firstly obtaining electricity market boundary data, meteorological observation data and line maintenance plan data of a target area, and carrying out the preprocessing to form multi-source feature input data; and then, inputting the data into an LASSO regression model for price difference prediction. The model comprises a dynamic penalty factor generator and a near-end gradient solver. The dynamic penalty factor generator dynamically adjusts penalty factors based on multi-source features and predicted price difference in the last time period. And the near-end gradient solver adopts a near-end gradient algorithm, combines a Nesterov acceleration strategy and convergence threshold judgment conditions, iteratively solves optimal parameters, and finally outputs a day-ahead-real-time price difference prediction result. According to the method, through dynamic adjustment and efficient solution, the prediction accuracy is improved.

The present invention provides a postoperative risk assessment system, device, storage medium and program product, relating to the field of computer technology. The system includes: an extraction module for extracting independent risk factors for early saphenous vein graft failure from the postoperative data of a patient; a score determination module for determining the scores of each factor in the independent risk factors based on the scoring scale in the nomogram model, where the nomogram model is constructed based on the independent risk factors; and a prediction module for predicting the risk information of early saphenous vein graft failure after coronary artery bypass grafting based on the risk coefficient corresponding to the total score of each factor in the independent risk factors. Based on the results of Lasso regression analysis and Logistic regression analysis, the present invention constructs a prediction model for early saphenous vein graft failure with risk factors such as QFR > 0.80, PI > 3.0, complications, and non-left main stenosis as the main body, thereby improving the accuracy of the risk assessment of early saphenous vein graft failure in postoperative patients.

A time series prediction method and apparatus for the field of energy, and a medium, which relate to the technical field of energy prediction. The method comprises: collecting power generation time series data and a trend picture, and converting the trend picture into time series data; constructing and training a Lasso regression model, and saving difference time series data between a time series and a prediction result of the Lasso regression model; pre-processing the time series data and the difference time series data; inputting the pre-processed data into a time-series large model for model training; and using the trained time-series large model to perform power generation time series prediction. In the present application, preliminary prediction is performed by means of a regression model, and a time series model is used to perform difference correction on the prediction result, thereby effectively improving the accuracy of the prediction result and the interpretability of the model.

The present application provides a kind of based on machine learning's mark metabolite model prediction method of optimal harvest period of Chinese yam, steps are as follows: collecting Chinese yam samples of different harvest periods, obtain metabolomics data by analyzing Chinese yam samples through metabolomics technology;Metabolomics data are preprocessed;The feature related to the growth period of Chinese yam is obtained by using machine learning algorithm to select potential marker metabolite;LASSO regression method is used to screen potential marker metabolite to construct marker metabolite prediction model;The area under ROC curve is used to verify the constructed marker metabolite prediction model;The metabolomics data of new Chinese yam are input into the marker metabolite prediction model to obtain model score, and whether Chinese yam is suitable for harvesting is judged according to model score.The present application can accurately predict the optimal harvest period of Chinese yam, eliminate subjectivity and experience dependence, improve scientificity, reduce external environmental influence, realize Chinese yam production capacity maximization, and provide reliable technical support for agricultural production.

The invention provides a papillary thyroid carcinoma neck lymph node metastasis risk prediction method and system based on blood indexes and TI-RADS grading, and relates to the technical field of medical data analysis. According to the method, an original data set is constructed by obtaining TI-RADS classification, the maximum diameter of nodules, the number of nodules, gender, age and conventional hematology indexes including apolipoprotein B and carcino-embryonic antigen of a patient, characteristic variables are screened by adopting LASSO regression, and an independent prediction risk factor model is established in combination with multi-factor Logistic regression. And further constructing a column graph model, carrying out performance verification through multiple statistical indexes, and deploying the model to a webpage calculator based on a shiyapp to realize convenient output of the individualized risk probability. According to the method, efficient and accurate prediction of the preoperative lymph node metastasis risk is realized, and reliable decision support can be provided for clinic.