136 results about "Lasso regression" patented technology

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 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 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 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.

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.

The invention discloses a neonatal congenital heart disease screening method based on machine learning, and the method comprises the following steps: 1, designing a questionnaire according to literatures related to adverse birth result risk factors, collecting infant cardiac ultrasound examination data, and then preprocessing the data; step 2, randomly dividing the data obtained in the step 1 into a training set and a test set, and performing dimension reduction processing on features by using lASSO regression; and step 3, based on the features obtained in the step 2, training a prediction model by using machine learning algorithms such as a random forest, a support vector machine, a lightweight gradient elevator, logistic regression and extreme gradient lifting. According to the method, the model is optimized through combination of multi-step data processing and various machine learning algorithms, related features can be comprehensively and scientifically screened, the accuracy of the prediction model is improved, a more reliable and effective method is provided for screening the neonatal congenital heart disease, early-stage accurate screening is facilitated, and the screening efficiency and quality are improved.

The invention belongs to the technical field of biological medicine, and discloses a portal cholangiocarcinoma postoperative risk prediction model based on a gamma-glutamyltransferase (GGT) dynamic change track and a construction method thereof. The method comprises the following steps: (1) collecting clinical characteristic data and survival characteristic data (including survival time and survival state) of a patient suffering from porta hepatis cholangiocarcinoma subjected to radical resection; 2) on the basis of GGT detection results at different time points in a perioperative period, dividing patients into different GGT dynamic change track types by using a latent category hybrid model; and 3) taking the clinical characteristic data and the GGT dynamic change track type as independent variables, taking survival data as response variables, screening prediction factors through LASSO regression, and constructing a multi-factor Cox regression model, namely a postoperative risk prediction model. According to the method, the dynamic evolution rule of the GGT is introduced, so that the limitation of single-time static detection is made up, the quantitative evaluation of the survival risk is realized, and a scientific basis is provided for individualized treatment and follow-up visit management.

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

The invention relates to the technical field of carbon emission transactions, in particular to an electric energy quotation decision-making method, system and device based on electric power prediction and a medium. The method comprises the steps of obtaining power grid prediction data, historical operation data and self-contained power supply basic data, and performing preprocessing to obtain a standardized feature data set; an elastic network model is constructed based on feature vectors, and by balancing between Ridge regression and Lasso regression, feature correlation information is reserved, and feature sparse selection is realized; and finally formulating a quotation strategy according to a prediction result and historical transaction data. According to the invention, the key correlation between the price and the operation of the self-contained power supply can be effectively identified, and reliable decision support is provided for enterprises.

Computer system (100) and method for generating a data driven interpretable pharmacometrics prediction model (150) to predict at least one medical state parameter (MSP1) of a patient (201) of a special population. The system obtains a low-dimensional NODE (110) having an autonomous part and a non-autonomous part. The NODE comprises a first neural network (NN1) trained for approximating the autonomous part (AD), and a second neural network (NN2) trained for approximating the non-autonomous part (NAD). Training comprises fitting the NODE (110) to respective data (211) of respective special population members. Derivative data (AD', NAD') generated from the neural networks (NN1, NN2) is computed within minimum and maximum values of observed medical state parameter values (OPR) and time values (OTI). Outputs (O1 to OM) of predefined commonly accepted pharmacometric components (PC1 to PCN) are fitted to the derivative data using LASSO regression. A particular combination (C1) of best-fit pharmacometric components (PC1, PC2) is identified as the data driven interpretable pharmacometric prediction model (150).

This invention discloses a method for predicting sudden cardiac death based on forensic autopsy data using machine learning and its forensic applications, belonging to the fields of machine learning, statistics, and forensic identification. This invention provides a method for screening independent predictors of forensic diagnosis of sudden cardiac death using LASSO regression and logistic regression. Using 10-fold cross-validation, the method selects the minimum lambda(λ) to identify 14 risk factors. Logistic regression ultimately identifies 9 independent predictors, including age, heart weight, left ventricular wall thickness, right ventricular wall thickness, interventricular septum thickness, aortic valve circumference, mitral valve circumference, liver weight, and left kidney weight. A nomogram and a web-based calculator are constructed for predicting sudden cardiac death in forensic practice, thereby determining sudden cardiac death using objective indicators. This method has not been reported worldwide.

The invention discloses a prognostic scoring method and system for predicting advanced biliary tract malignant tumor immunotherapy, and belongs to the technical field of advanced biliary tract malignant tumor immunotherapy. The method comprises the following steps: firstly, generating random replacement copies for real variables to obtain shadow variables, and classifying the real variables into related classes by comparing importance scores of the random replacement copies and the shadow variables; real variables in related classes are screened based on LASSO regression coefficients, and a variable set is formed; combining the variables in the set, and screening out a variable combination corresponding to a minimum AIC value model according to an AIC criterion to obtain a prognosis key variable set; calculating an index score according to the COX regression coefficient of each real variable; and finally, adding the index scores of the variables in the prognosis key variable set of each patient to obtain a prognosis BICCAPS score. Through three-step progressive screening of the random forest algorithm, the LASSO and the optimal subset method, the problems of variable screening, colinearity and model conciseness in high-dimensional clinical data modeling are solved.

This invention relates to the field of intrinsic ability prediction technology and discloses a method for predicting the intrinsic ability characteristics of the elderly based on machine learning. First, intrinsic ability score data of the elderly in five dimensions, including cognition and psychology, are acquired, and heterogeneity classification is performed using latent profile analysis. Using the classification results as labels, random forest and LASSO regression are used in parallel to screen key predictive factors to form a feature subset. A classification prediction model is built based on XGBoost, and hyperparameters are optimized through cross-validation and grid search. The SHAP and LIME algorithms are integrated to achieve both global and local interpretability. Finally, the model is deployed on an online interactive platform, where inputting individual characteristics outputs the IC propensity classification, probability, and feature contribution explanation. This invention achieves accurate classification and interpretable prediction of the intrinsic abilities of the elderly, reduces the dependence of assessment on professionals and the environment, and is suitable for large-scale application in grassroots elderly care scenarios.