100 results about "Logistische regression" patented technology

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 method for monitoring parameters of a patient during surgery with extracorporeal circulation serves to estimate the presence of AKI risk and comprises calculating a dynamic global index of AKI risk as a logistic regression deriving from a static AKI risk index and a dynamic index of AKI risk. The value and / or the time trend of such global index of AKI risk can be advantageously displayed on a monitor during the surgery. The dynamic AKI risk index is calculated at least on the basis of the extracorporeal circulation time, the minimum level of oxygen supply and the exposure time to oxygen supply below a critical threshold which are measured or determined repeatedly during surgery. Preferably, the dynamic AKI risk index is also calculated on the basis of the minimum mean arterial pressure during surgery and / or the maximum concentration of lactate in the blood during surgery and / or the minimum hematocrit during surgery and / or the fact that the patient has been subjected to transfusion(s) during the extracorporeal circulation.

The invention discloses an early-stage lung cancer prediction method based on a multi-modal eccDNA marker, and relates to the technical field of liquid biopsy, and the method comprises the following steps: collecting a peripheral blood sample, carrying out differential centrifugal separation on plasma, constructing a cfDNA library, and carrying out double-end sequencing to obtain original sequencing data; extracting structural features through an eccDNA model, and inputting the structural features into a first machine learning model to generate a circular DNA score; extracting variation features through an SNV model, and inputting the variation features into a second machine learning model to generate a spectrum feature score; extracting copy number features through a CNV model, and inputting the copy number features into a third machine learning model to generate copy number scores; carrying out probability distribution calibration on the circular DNA score, the spectrum feature score and the copy number score; inputting the three types of molecular features into a deep neural network to generate a first fusion score; inputting the calibrated score into a logistic regression model to generate a second fusion score; and generating a final lung cancer risk probability according to the first fusion score and the second fusion score.

The application discloses an evaluation method for predicting the safety of EGFR TKIs monotherapy for NSCLC. The method comprises the following steps: obtaining the data of a plurality of patients receiving EGFR TKIs treatment; taking MDRAE (maximum grade of drug-related adverse event) as a safety index, constructing a safety evaluation final model, and the final model is an ordered logistic regression model, the covariates of the final model include normalized steady-state trough concentration, EGFR TKIs treatment history, gender, baseline glutamyltransferase, baseline uric acid, baseline creatine kinase, baseline platelets and baseline lymphocytes; obtaining the data of a target patient, inputting the numerical values of each covariate in the data of the target patient into the final model; determining the first probability of MDRAE of the target patient being grade 3 and above, the second probability of MDRAE being grade 2 and the third probability of MDRAE being grade 1 and below according to the final model, and taking the grade corresponding to the maximum probability in the three probabilities as the predicted grade of MDRAE of the target patient. The application improves the accuracy of predicting the safety of EGFR TKIs monotherapy for NSCLC.

The invention relates to the technical field of medical imaging, in particular to a breast cancer tissue HER2 automatic scoring method and device based on a characteristic curve, and the method comprises the steps: carrying out the reading operation of a full-slice image of immunohistochemical staining; extracting a target region of interest of the full slice image; slicing and screening the target region of interest to obtain a plurality of image slices; calculating a percentage-saturation characteristic curve of each image slice, and extracting characteristic information of the percentage-saturation characteristic curve; based on the feature information of the percentage-saturation feature curve, performing preliminary HER2 scoring on each image slice in combination with a rule base and a logistic regression classification algorithm; and integrating the scoring results of the image slices, and outputting a final HER2 score and a characteristic curve visualization graph. According to the method, the feature curve is combined with the rule base and the logistic regression classification algorithm, so that the objectivity of HER2 scoring is improved, and the problems of subjectivity and low efficiency of manual evaluation are reduced.

The invention provides a privacy protection calculation method based on group learning and cardiovascular disease prediction, and the method comprises the steps: obtaining local medical data, carrying out the preprocessing, and dividing the data into a training set and a test set; training at least one base learner according to the local data characteristics and outputting a prediction probability; a Gaussian difference privacy mechanism is adopted to disturb prediction output; dynamically adjusting the privacy budget epsilon in an exponential decay mode according to the number of rounds in the training process; receiving prediction results after disturbance of all the clients and aggregating the prediction results by using a logistic regression model as a meta-learner; training a lightweight student model through knowledge distillation; the prediction capability is evaluated according to multiple performance indexes, and the influence degree of the differential privacy mechanism on the model performance is verified through a model test result under multiple epsilon values; and constructing an attack model to evaluate the protection effect of the privacy mechanism. On the premise of ensuring data privacy security, the cardiovascular disease prediction capability is improved, and the optimal balance between privacy protection and model performance is realized.

The invention relates to the technical field of bioinformatics and medical data processing, and discloses a polygene molecular diagnosis model and a construction method and application thereof.The construction method comprises the steps that a data acquisition module constructs a genome, clinical phenotype and environmental exposure data matrix, a preprocessing module executes regression filling and standardizes continuous variables, and a data processing module performs data processing; the feature screening module executes double-layer screening by using LASSO and a random forest model to output a core feature subset, the model building module builds a logic regression architecture to calculate a baseline logarithm probability, and the dynamic updating module outputs a real-time risk probability in combination with follow-up visit environment data, a time adjustment coefficient and the baseline logarithm probability. And the interactive output module outputs a risk layering label and a feature contribution degree. According to the method, redundancy is eliminated through double-layer screening, the time dimension is introduced to adjust the real-time correction probability, visual attribution is realized in combination with the SHAP algorithm, and the dynamic monitoring capability and interpretability are improved.

The application discloses a classification method and device for pancreatic neuroendocrine tumors. The classification method comprises the following steps: segmenting tumor masks from enhanced CT images; for each tumor mask, generating a first peritumoral region and a second peritumoral region; dividing the tumor into multiple habitats for each tumor mask; extracting 3D radiomics features, multiple peritumoral microenvironment features, multiple tumor habitat features, multiple local pathological features and multiple global context features; selecting multiple target features for each enhanced CT image and calculating a radiomics score; inputting the radiomics score and clinical variables into a logistic regression model for training to obtain a trained logistic regression model; and using the trained logistic regression model to classify the enhanced CT images to be classified. The application can quickly and accurately realize G-grade classification of pancreatic neuroendocrine tumors, and provides a reliable non-invasive evaluation tool for clinical decision-making.

This invention relates to an MRI-based method for classifying benign and malignant intraductal lesions (IDLs) of the breast, comprising the following steps: Step 1) Extracting a set of clinical features from MRI images: age, ADC value, BI-RADS category, lesion shape, margin clarity, and TIC curve type; Step 2) Extracting radiomics features from the 3D lesion segments of the T1 DCE-MRI sequence; Step 3) Reducing the dimensionality of the radiomics features using the Lasso algorithm; Step 4) Fusing the dimensionality-reduced radiomics features with the clinical features into an input vector; Step 5) Outputting benign / malignant probability values ​​through a logistic regression model; Step 6) Applying stratification rules to BI-RADS 4A lesions: older patients or those with low ADC values ​​are marked as high-risk and biopsy is recommended; younger patients with high ADC values ​​are marked as low-risk and follow-up is recommended. This method solves the problem of current dynamic contrast-enhanced MRI's difficulty in accurately identifying BI-RADS categories, especially the BI-RADS 4A high-risk subgroup, thereby reducing missed diagnoses and over-biopsy. It is of urgent significance for overcoming the bottleneck of preoperative risk stratification in IPLs and guiding individualized clinical decision-making.

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 specifically discloses a medication safety assessment method and system for hypertensive patients, and the method comprises the following steps: extracting one-complaint five-history, medication records, inspection indexes and outcome data of the hypertensive patients from an electronic health record system (EHR), carrying out the preprocessing, extracting key entities, and carrying out the verification of the key entities; constructing a comprehensive data set of'co-disease-medication-examination-symptom 'of the patient, identifying a co-disease combination strongest associated with hypertension, and mining specific adverse reaction signals under the co-disease combination; key factors causing adverse reactions or unreasonable medication are attributed through a multi-factor conditional logic regression or decision tree model; and based on common disease combination and multi-factor logistic regression analysis of one complaint and five histories, performing pairing t inspection on laboratory indexes, and generating a medication safety report of the hypertension common disease patient. By adopting the technical scheme, through multi-dimensional data analysis, specific influence factors causing adverse drug reaction or unreasonable drug use are attributed, and accurate drug use safety guidance is provided for clinicians.

This invention provides a method for analyzing the radiomics characteristics of medullary thyroid carcinoma based on ultrasound, relating to the field of image or video recognition or understanding technology. The method includes: obtaining data from thyroid surgeries at various target hospitals to form a backup dataset; filtering the backup dataset to obtain a target dataset; labeling the data in the target dataset with Regions of Interest (ROIs); performing feature extraction and feature filtering on the data in the target dataset to obtain training and validation sets; constructing a target radiomics feature model; calculating radiomics feature scores based on the target radiomics feature model; obtaining a clinical feature model, an ultrasound feature model, a comprehensive model, and a scoring model based on logistic regression analysis; and performing radiomics feature analysis based on the clinical feature model, ultrasound feature model, comprehensive model, and scoring model. This invention solves the problems of low diagnostic accuracy and high requirements for experience of image interpreters in existing technologies for thyroid nodules.

The application provides a breast cancer patient lymph node metastasis prediction model without clinical pathological characteristics, comprising the following steps: after three-dimensional reconstruction of two-dimensional lung enhanced CT films, an axillary lymph node atlas is established, all axillary lymph nodes in the atlas are selected as ROI regions, and more than 5 combined image features of each axillary lymph node are selected to distinguish whether breast cancer has axillary lymph node metastasis; and a logistic regression machine learning prediction model is used to construct the breast cancer patient axillary lymph node metastasis prediction model. The model established by the application can non-invasively predict whether breast cancer has axillary lymph node metastasis, the clinical pathological characteristics of the patient are not included in the model, and the image cutting in the model is not based on breast tumors, but based on axillary lymph nodes; the model is used to determine a suitable axillary treatment scheme, thereby avoiding unnecessary axillary surgery and complications, and helping to carry out more accurate surgery and adjuvant therapy mode of breast cancer.

This invention discloses a method for identifying intrahepatic cholangiocarcinoma (ICC) based on serum peptide characteristics and its application. The invention collects serum samples from three groups: individuals with ICC, those with benign liver disease, and healthy controls. Peptide characteristic peaks associated with ICC are screened, and a model is constructed using a logistic regression algorithm. This model demonstrates excellent discriminative ability on both the training and independent test sets, with AUCs reaching 0.986 and 0.963, respectively, significantly outperforming traditional tumor markers CA19-9 and CEA. The peptide characteristic peak combination and the discriminative model constructed based on it can be used for early detection of ICC, differentiation between benign and malignant cases, and screening of high-risk populations. It offers advantages such as rapid detection, standardized procedures, strong early ICC identification capability, and low false positive rate, providing a highly sensitive, specific, and widely applicable non-invasive screening tool for clinical use.

The invention relates to application of a reagent for evaluating the recurrence risk of epithelial ovarian cancer, and belongs to the technical field of molecular biology. The application specifically refers to application of a reagent for specifically detecting the expression level of SNORD18C and / or SNORD84 in an epithelial ovarian cancer operation tissue sample in preparation of a kit for evaluating the recurrence risk of an epithelial ovarian cancer patient subjected to resection. The change of the expression quantity of SNORD18C and SNORD84 in an epithelial ovarian cancer operation tissue sample is found, and the change of the expression quantity is closely related to the recurrence risk of an epithelial ovarian cancer patient after the epithelial ovarian cancer patient is subjected to resection. Therefore, by detecting the expression of SNORD18C and / or SNORD84 in an epithelial ovarian cancer tissue sample and combining the binomial logistic regression model, the recurrence risk of an epithelial ovarian cancer patient after the resection is assessed, and high sensitivity and specificity are achieved.

The invention relates to a method implemented by a computer device for detecting at least one lesion of the pancreas of a patient in at least one medical image, such as portal phase computed tomography (CT). And training the segmentation network in a five-fold cross validation mode. The outputs of the network are then post-processed to extract image features: normalize lesion risks, predict lesion diameters, and MPD diameters of the head, body, and tail of the pancreas. A logistic regression model is calibrated to predict the presence of a lesion based on the features.

The present application relates to the technical field of biological medicine, in particular to a lipid metabolite combination for early diagnosis of VKH and application thereof, by obtaining plasma samples of patients with initial acute stage (early stage) VKH syndrome and healthy controls, and constructing sample-full lipid quantitative expression matrix, screening differential lipids through PCA unsupervised analysis and OPLS-DA supervised model, further screening lipids with high diagnostic performance by using elastic net logistic regression model, finally obtaining a diagnostic marker combination composed of 25 lipid metabolites, solving the technical problems of existing VKH diagnosis technology, such as invasiveness, insufficient sensitivity and stability of protein marker detection, limited diagnostic efficiency, lack of systematicness and standardized process in lipidomics research, achieving the effect of non-invasive, efficient, high sensitivity and high specificity of early auxiliary diagnosis of VKH syndrome, and providing objective and reliable technical means for precise identification of atypical cases and large-scale clinical screening.

The application provides a marker for diagnosing primary aldosteronism, a model and application thereof, and combines a univariate analysis and a logistic regression analysis method, analyzes primary aldosteronism samples and primary hypertension samples in a national multicenter cohort, screens out a marker that can be used for predicting whether an individual has primary aldosteronism, and further utilizes a decision tree algorithm to construct a prediction model, realizes intelligent, rapid and simple prediction and diagnosis of primary aldosteronism based on LC-MS / MS technology and the prediction model, and meets clinical requirements.

The application discloses a methylation marker combination for detecting early gastric cancer and application thereof. The marker combination comprises 146 methylation sites of 11 DNA regions and a transformation rate control marker region, and the methylation levels of the target regions are significantly different between early gastric cancer patients and healthy people. The kit of the application contains reagents for detecting the methylation degree of the above sites, and supports various high-throughput sequencing methods. The detection system adopts a logistic regression model to calculate a prediction value, multiplies the average methylation rate of each region by a corresponding weight coefficient, adds them together, and compares the sum with a threshold value to make a positive judgment. The application realizes high sensitivity and high specificity for non-invasive early detection, is particularly suitable for non-invasive screening of plasma free DNA samples, can effectively improve the survival rate of gastric cancer patients, can significantly reduce medical expenses, and has a wide clinical application and industrial utilization prospect.

The application discloses a prediction model of the risk of atherosclerosis of a type 2 diabetes patient and a construction method thereof, and the construction method comprises the following steps: obtaining general information, serum biochemical indexes and a brachial-ankle pulse wave velocity of the type 2 diabetes patient; calculating a ratio of visceral fat area and subcutaneous fat area of the patient; comparing the correlation of the general information and the serum biochemical indexes by using a single factor variance and a chi-square test method to obtain variables with statistical differences; determining influencing factors of the type 2 diabetes patient combined with atherosclerosis by using Spearman correlation analysis and multivariate logistic regression analysis; and constructing a logistic regression model: Logit (H) =-4.647+1.013*V / S+0.099*age-0.527*smoking+0.67*Hypertension-0.007*Cr, namely, the prediction model. The prediction model has good prediction performance and high clinical application value.

The invention discloses a non-invasive colorectal cancer early screening and diagnosis method and electronic equipment. The method comprises the following steps: collecting excrement, saliva, a blood sample and a standardized tongue image of a subject; species abundance information of intestinal flora and oral flora is obtained through metagenome sequencing, and the tumor marker level is obtained through blood detection; and inputting the multi-modal data into a pre-trained colorectal cancer risk prediction model, and outputting a risk probability. The model is obtained by training based on a machine learning algorithm by adopting a training data set containing healthy persons and patients, the architecture of the model adopts a late fusion strategy, clinical, flora and tongue picture features are respectively processed through three sub-models of logistic regression, a gradient boosting decision tree and a convolutional neural network, dynamic fusion is performed by utilizing a Self-Attention mechanism, and finally a diagnosis result is output. According to the method, through multi-modal information fusion, the screening accuracy, the standardization degree and the patient compliance are remarkably improved.

The invention relates to the technical field of biological medicines, and provides a plasma metabolite-based ALD non-invasive differential diagnosis model, which is characterized in that five key metabolites in plasma, namely vecuronium bromide, N-(3-amino-3-oxopropyl)-L-valine, geranyl citronellol, phenylpropanolamine and S-allylcysteine, are used as core diagnosis indexes; or by taking alanine aminotransferase as an auxiliary index at the same time, calculating the ALD disease probability through a logistic regression equation. The invention further provides a construction method and application of the model. The method has the advantages that by accurately capturing plasma metabolite differences and combining clinical indexes, efficient identification of healthy people and ALD patients is achieved, and a reliable tool is provided for early screening and clinical diagnosis of ALD.

This invention relates to a method, system, and storage medium for processing liver imaging data based on Gd-EOB-DTPA enhanced MRI. By acquiring multi-phase Gd-EOB-DTPA enhanced MRI image data and clinical laboratory data, the method standardizes and extracts regions of interest from the liver parenchyma and reference tissues, calculates and selects the optimal combination of MRI image parameters, and fuses these image parameters with blood biochemical indicators to construct a multimodal feature set. A model library is built using linear support vector machines, weighted K-nearest neighbors, and efficient logistic regression. Model training, validation, and testing are completed through stratified sampling and cross-validation to obtain a stable and reliable liver function grading assessment model, ultimately achieving intelligent, non-invasive, and quantitative assessment of ALBI grading. Compared with existing technologies, this invention has advantages such as being non-invasive, precise, standardized, interpretable, and easily clinically translated.

The application discloses a novel coronavirus myocardial injury early prediction method based on mitochondrial function, and the method comprises the following steps: determining a sampling object and completing sample collection; collecting node index data including mitochondrial membrane potential, ROS generation level, ATP content, mtDNA release level and mPTP opening degree; completing feature importance evaluation on all node index data, outputting importance scores of each node and interaction relationship, and calibrating pathological stage weight factors in time sequence logistic regression; reserving node progressive correlation penalty items in the time sequence logistic regression layer, calculating myocardial injury risk probability of a single sub-model by integrating node trigger state, time decay weight and penalty items; and integrating output results of multiple time sequence logistic regression sub-models to obtain a final risk prediction value, so that the final prediction has robustness and pathological fitting degree.

The invention discloses a model for early prediction of acute kidney injury risk after geriatric sepsis patients are transferred into ICU based on clinical variables and immune inflammation indexes, 627 geriatric sepsis patients are included in the research, and the clinical variables and immune inflammation indexes of the geriatric sepsis patients in the ICU within 24 hours are collected; clinical variables of the first 6 ranked and immune inflammation indexes of the first 4 ranked related to acute kidney injury are screened out, prediction models are constructed through four machine learning algorithms respectively, the model prediction performance of the XGBoost algorithm is the best, the model prediction performance of the logistic regression algorithm is the second, and the model prediction performance of the XGBoost algorithm is the best. And a visual column diagram is made according to a logistic regression algorithm, and an early prediction model with good accuracy is established.

The application discloses a lung cancer hemoptysis prediction method based on GWO double-parameter optimization, which first outlines the region of interest of the CT image of a lung cancer patient, extracts the image feature and completes the initial screening of feature stability and intergroup difference; then, a grey wolf optimization algorithm is used to map the two-dimensional position vector of the grey wolf individual into the combination of the LASSO penalty coefficient λ and the logistic regression regularization parameter C1; the average AUC of k-fold stratified cross-validation is used as the objective function to perform iterative optimization on the training set, the optimal alpha wolf is determined, and the population position is updated; after the maximum number of iterations is reached, the global optimal parameter combination is output, the LASSO model is fitted based on the optimal λ to perform feature screening, and the optimal C1 is used to construct the final logistic regression prediction model. The application realizes double-parameter joint optimization through GWO, solves the problem that the traditional step-by-step parameter adjustment is easy to fall into local optimization and has a high risk of overfitting, improves the model accuracy and generalization ability, and can non-invasively, early and quantitatively predict the lung cancer hemoptysis risk to assist clinical decision-making.

The invention provides a lightweight model for monitoring VTE of a cervical cancer patient based on fusion of time features and immune indexes. The model is characterized in that eight key features with the highest contribution degree, namely D-dimers, FDP, WBC, HB, PLT, CD4, CD8 and the ratio of CD4 to CD8, are extracted through a pre-trained CliTsRNN model; encoding the time sequence of the key features into a time feature vector of'static value + dynamic change rate '; the method is obtained by adopting logistic regression training and has the characteristics of accuracy, high efficiency, interpretability, light weight and the like, the AUC reaches 0.918, the accuracy rate reaches 0.866, the F1 score reaches 0.864, the recall rate reaches 0.854, the accuracy rate reaches 0.875, the inference speed is smaller than 0.0001 s / example, the model is smaller than 50 MB, key factor sorting is supported, and the auditing requirements of evidence-based medicine are met.

The application discloses a methylation marker combination for detecting early esophageal cancer and application thereof. The marker combination comprises 178 methylation sites of 12 specific DNA regions and a specific internal marker region, and the methylation levels of the regions in plasma of patients with early esophageal cancer and healthy people are significantly different. The application also provides a primer combination and a kit for detecting the marker combination, and supports high-throughput deep sequencing. A detection system is used to extract plasma free DNA, perform bisulfite conversion and amplification, calculate a prediction value by using a logistic regression model, multiply the average methylation rate of each region by a corresponding weight coefficient, and compare the result with a threshold value to make a positive judgment. The application greatly improves the detection sensitivity of non-invasive screening for early esophageal cancer under the premise of ensuring high specificity, and has a wide clinical application prospect.

The application discloses a protein marker for predicting iodine uptake capacity of a thyroid cancer metastatic focus, a prediction method and a system, and belongs to the technical field of tumor medicine. The application provides 23 protein markers capable of being used for predicting iodine uptake capacity of a thyroid cancer metastatic focus, and the specific prediction method is as follows: the expression level of the protein marker in the metastatic focus tissue is obtained; the protein expression amount or abundance value is subjected to data preprocessing and standardization; the standardized protein expression amount is input into a pre-trained logistic regression model to obtain an iodine uptake positive probability value of the thyroid cancer metastatic focus, and the iodine uptake capacity of the sample is judged based on pre-determined classification threshold comparison. The method can depict iodine uptake related characteristics of the metastatic focus at a molecular level, provides an objective, quantifiable and reproducible iodine uptake capacity prediction scheme, provides an effective auxiliary basis for radioiodine treatment decision and patient risk stratification, and has clear application value and popularization significance.

The invention discloses a cholestasis index-based liver cancer risk prediction model construction method, which comprises the following steps: collecting chronic HBV infected person data containing 16 parameters, carrying out desensitization, carrying out stratified random sampling to divide a data set, and carrying out two-stage screening to obtain HBsAg, ALT, ALP, GGT, PLT and AFP core features; after Min-Max Scaling processing, L1 regularization logistic regression is combined with grid search and 5-fold cross validation to construct the model, and the model constructed by the method is obviously superior to a traditional CU-HCC model and a REACH-B model, especially has higher recognition capability for early HBV-PLC, and can effectively reduce the missed diagnosis rate.