17 results about "Survival data" patented technology

The invention provides a gene marker combination and system for predicting glioma risk and application, and belongs to the technical field of glioma prediction. The gene marker combination comprises an NTN4 gene, an RBP1 gene, a TWIST1 gene, a GADD45G gene, an NUAK2 gene, a GRIK2 gene, a WEE1 gene and an RRM2 gene. Eight senescence-related genes are screened by screening senescence-related genes and combining survival data of glioma patients. The research finds that the risk scoring model constructed by using the eight senescence-related genes can specifically and sensitively predict the glioma risk, and has good prediction efficiency. According to the method, the glioma risk assessment model constructed on the basis of combining the risk scoring model with the clinical pathological variable factors is established, and compared with the capability of predicting the glioma risk of the risk scoring model, the glioma risk assessment model is better in prediction efficiency.

The present disclosure provides an establishment and use of index for evaluating malignancy level of tissue microenvironment, which can be used to detect the potential of an individual to develop precancerous lesions and to predict the recurrence of tumors without relying on patient survival data.

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 discloses a kind of based on artificial intelligence's pharmaceutical clinical test safety evaluation method, comprising the following steps: collecting heterogeneous clinical survival data with deletion characteristics and time series drift characteristics, and constructing original survival data set;Based on propensity score model and survival outcome model, implement deletion correction, obtain individual non-compliance risk score;Through stratified weighting rule and two-side order-preserving prediction mechanism, calibrate and update individual risk prediction interval;Calculate individual risk upper and lower bound probability, define three-domain risk threshold rule, generate real-time risk decision result through three-domain decision mapping and dynamically evaluate overall safety trend.The application improves the robustness and interpretability of clinical safety risk decision.

The invention relates to the technical field of feature screening, in particular to a multi-label data feature screening method and system based on an immune genetic algorithm. Each label population is constructed by single-label prior features, and recognition of complex information between features-features, features-labels and labels is realized through multi-population parallel operation, immunization, elite retention, concentration inhibition and global feature migration strategies. Therefore, the optimal feature subset screened out by each label has a good prediction effect on the corresponding label result, and the accuracy of multi-label medical disease prediction is improved. By constructing classification and survival multi-label models and calculating chromosome fitness values, the method can be simultaneously suitable for feature screening of medical multi-label classification and survival data.

The present invention relates to the technical field of medical informatization, and in particular to a neurosurgical disease diagnosis and prognosis prediction system and method based on machine learning. The system includes six modules: a data collection module, a data processing module, a disease diagnosis module, a prognosis prediction module, a model evaluation module, and a result output module. The system integrates clinical data, image data and survival data. After preprocessing, screening and feature extraction, support vector machine and random forest algorithms are used to perform disease classification and prognosis prediction. By comparing the results of the algorithms, a diagnosis and a prognosis prediction are obtained. The model evaluation module dynamically adjusts the weights of the algorithms to optimize the prediction accuracy. Finally, the system integrates diagnosis, prediction and evaluation results to generate a visual diagnostic report. This innovative design enables multi-source data fusion, multi-model collaboration and dynamic optimization, significantly improves the diagnostic accuracy and prediction reliability, provides strong support for clinical decision-making, and is expected to play an important role in improving the level of diagnosis and treatment, improving patient prognosis, and optimizing the allocation of medical resources.

A computing device includes a memory storing at least one program, and a processor configured to perform at least one operation by executing the at least one program, wherein the processor is configured to generate virtual data including information about survival rates of virtual patients included in a first group, based on pre-generated survival data, generate control group data by classifying each of the virtual patients as a responder or a non-responder according to a certain criterion, generate experimental group data based on at least one of medical images and survival data of actual patients included in a second group to which a specific regime has been applied, and output a result of comparison between the control group data and the experimental group data.

The invention discloses a lung cancer lifetime prediction model training method based on feature selection, and relates to the technical field of model training. Comprising the steps that feature information associated with the lung cancer lifetime is acquired, a target feature set is obtained, and the target feature set at least comprises a target feature item composed of one piece of feature information; the target feature set is screened through a screening method, low-correlation feature information is removed, a screened feature set is obtained, and the screened feature set is used for representing a high-correlation feature information set. According to the method, the feature information associated with the survival time of the lung cancer is obtained, the feature information is screened, the feature information with low association degree is removed, the screened feature information is matched and combined, the mutual influence combination result of multiple pieces of feature information is obtained, the feature matching space is constructed, and the survival data of the lung cancer is obtained as a contrast. And learning the nonlinear combination utility of the feature matching items through a neural network hidden layer, and training the model according to the nonlinear combination utility.

The invention discloses a method for constructing a lung adenocarcinoma diagnosis model based on immunoassay and interpretable machine learning, and belongs to the field of biomedical engineering.The method comprises the steps that data acquisition and preprocessing are conducted, specifically, an experimental data set containing lung adenocarcinoma sample gene expression data, survival data, immune cell infiltration abundance data and immune checkpoint gene data is acquired, the verification data set comprises expression data of lung adenocarcinoma tumor tissue and normal tissue samples, and preprocessing the data; according to the invention, genes are screened through immunization-survival dual association, and the diagnosis specificity is improved in combination with lung adenocarcinoma immune microenvironment characteristics; according to the invention, contribution (such as specific influence of CDCA8, SPAG5 and other genes on diagnosis) of a core gene is determined through an SHAP model, and the problem of black box in traditional machine learning is solved; therefore, the method can be used for lung adenocarcinoma diagnosis, and an immune correlation analysis and multi-algorithm screening framework can be popularized to construction of diagnosis models of other tumors.

The invention relates to the technical field of survival data fitting of lung cancer treatment, and particularly discloses a survival data fitting method and an intelligent system for treating advanced non-squamous and non-small cell lung cancer by combining bevacizumab with cis-platinum. Comprising the following steps: a data acquisition step: collecting clinical data of a patient who treats the advanced non-squamous non-small cell lung cancer by combining bevacizumab with cis-platinum, the clinical data comprising basic information of the patient, treatment scheme information, survival time data and survival state data; a data preprocessing step: cleaning the collected clinical data, removing missing values and abnormal values, and performing standardization processing on the data; and a feature engineering step: performing feature engineering based on the preprocessed clinical data. According to the survival data fitting method and system for treating the advanced non-squamous non-small cell lung cancer by combining bevacizumab with cis-platinum, the limitation that only part of clinical characteristics are utilized in a traditional method is changed, complex data can be processed more comprehensively, and the accuracy and reliability of survival data fitting are effectively improved.

The invention provides a model hypothesis-independent survival analysis marker discovery method and device, and the method comprises the steps: collecting the follow-up survival data of a plurality of patients, and building a survival data set, samples of the data set comprise multi-dimensional features of biomarkers reflecting prognosis conditions of patients, actual observation time and labels indicating whether interested events are observed or not; constructing a model-free survival analysis marker selection optimization model, wherein the optimization model comprises a nucleation survival outcome dependency module for quantifying a dependency relationship between each feature and a survival outcome and a nucleation minimum redundancy module for eliminating redundant features; and solving the optimization model by utilizing the survival data set to obtain a screening result of the biomarker for predicting the prognosis condition of the patient. According to the method, the biomarker combination with high predictability and low redundancy can be effectively screened out under the condition that a preset model form is not needed, so that the discrimination capability and interpretability of the biomarker combination are improved.

The invention discloses an information processing method, device and equipment for individual prognosis gene recognition and a medium, relates to the field of artificial intelligence and biomedicine crossing, is applied to a computer device, and comprises the following steps: screening out undetermined genes belonging to a shared causal gene set from a cross-section gene sequencing sample of a current object; the shared causal genes in the set are genes obtained by performing survival distribution simulation and independence test on cross-section genes and survival data of historical objects to eliminate hybrid associated genes; inputting the actual expression quantity of the to-be-determined gene into the target gene expression prediction model to output the expected expression quantity of the to-be-determined gene, and determining the difference between the actual expression quantity and the expected expression quantity of the to-be-determined gene as the residual error of the to-be-determined gene; and comparing the total gene expression residual distribution with the residual of the to-be-determined genes so as to identify the individual prognosis genes of the current object exceeding a preset group normal fluctuation range from the to-be-determined genes. And completing accurate individual prognosis gene identification suitable for each group on the basis of the cross-section gene data.

The invention discloses a tumor patient survival prediction method. The method comprises the following steps: extracting Hamp; e, the dyed WSI features are obtained; processing the full slice image WSIs; the method comprises the following steps: extracting coding information of gene sequence data in space and time sequence dimensions by using a CNN (convolutional neural network) and a BiLSTM (bidirectional long-short term memory) neural network; extracting coding features of the slices and the multiple omics in dimensions such as time sequence, space and channel, establishing interaction and alignment between multi-omics text coding features and slice visual coding features, and mining common semantics of features among different modes; and carrying out survival prediction from the survival data of the right-censored case patients. According to the method, interference of irrelevant information can be weakened, tumor global and local spatial heterogeneity information in WSI can be effectively extracted, and meanwhile, the robustness of a risk division layering result is verified by visualizing the immune response difference between high and low risk division patients through the IOBR.

The application discloses application of a group of biomarkers in preparation of products for survival prognosis prediction of non-small cell lung cancer patients, wherein the biomarkers contain PD-L1, CTLA-4, CD226, Nectin-2 and / or CD276. And based on immunohistochemical semi-quantification of the above biomarkers in non-small cell lung cancer patients, combined with survival data of non-small cell lung cancer patients, LASSO-Cox regression analysis is conducted to construct a risk score formula, which can effectively distinguish non-small cell lung cancer patients into high-risk patients and low-risk patients, and the high-risk patients distinguished are non-small cell lung cancer patients who can benefit from chemotherapy combined with immunotherapy.

This invention relates to a method for predicting the prognosis of lung cancer based on multi-omics data fusion, comprising: acquiring multi-omics data and survival data of lung cancer patients, and preprocessing the multi-omics data of each lung cancer patient; concatenating the multi-omics data of each lung cancer patient to obtain a multi-omics ensemble feature matrix for each training sample; mapping the multi-omics ensemble feature matrix of the training samples to a low-dimensional space using an autoencoder to obtain a low-dimensional multi-omics feature matrix of the training samples; performing feature selection on the low-dimensional multi-omics feature matrix of the training samples using a VSOEnetbag algorithm combining elastic networks and bagging ideas based on the survival data of lung cancer patients to obtain a salient expression feature matrix of the training samples; performing risk subtype clustering on the training samples using K-means clustering based on the salient feature expression matrix of the training samples to obtain subtype clustering results; using the subtype clustering results of the training samples as labels and the RNA-Seq expression profile data of lung cancer patients as independent variables to construct a multivariate Cox prognostic model; inputting the RNA-Seq expression profile data of the lung cancer patients to be tested into the multivariate Cox prognostic model to obtain the prognostic results of the lung cancer patients.

The present application relates to the technical field of diabetes complication health management, in particular to a diabetes complication management method and system based on multi-modal data fusion and reinforcement learning, the method acquires continuous glucose monitoring high-frequency data, low-frequency discrete clinical indicators and survival data; uses a sliding window and a time sequence alignment algorithm to perform feature extraction and interpolation, and constructs a time-varying covariant state vector; uses a Cox proportional risk model containing an L1 regularization term to solve the optimal regression coefficient and calculate the instantaneous risk ratio, and then constructs a dynamic nomogram model to map the disease-free survival probability; the state vector, survival probability and risk ratio are input into the policy network of a reinforcement learning intelligent agent as the environment state, and the optimal intervention strategy is output in the preset action space; finally, the risk improvement indicators of the last moment and the current moment are used as reward signals to update the network parameters in a closed loop. The present application solves the problem of heterogeneous data fusion and realizes safe and personalized dynamic closed-loop health management.

This application discloses a data processing method, apparatus, device, storage medium, and program product. The method includes: performing self-supervised contrastive representation learning based on multiple samples to obtain a first encoder; the multiple samples include unlabeled image data of multiple users, and the first encoder is an updated base encoder; performing supervised contrastive representation learning based on the first encoder and supervisory information to obtain a second encoder; the supervisory information includes survival data of the multiple users, the survival data including temporal event data of censored and uncensored samples, and the second encoder is the updated first encoder; determining a prognostic assessment model based on the second encoder; the prognostic assessment model is used to predict the prognostic risk of users; performing prognostic analysis on users based on the prognostic assessment model, and outputting the prognostic analysis results.