45 results about "Survival analysis" patented technology

The invention discloses a numerical control machine tool wear monitoring and intelligent compensation control method based on deep learning, and relates to the field of numerical control machine tool wear monitoring. Multi-source data are collected through a sensor, and are preprocessed and fused through edge calculation; deep features are extracted and enhanced through an improved network, the abrasion state is evaluated through a mixed expert model, and life is predicted in combination with survival analysis; based on enhanced transfer learning, generating an intelligent compensation strategy according to a processing target, and executing the intelligent compensation strategy after verification in a virtual environment; a closed-loop system is constructed, all modules are acquired, fed back and optimized, and full-process intelligent management of functions such as knowledge graph early warning and multi-machine-tool cooperation is integrated. According to the method, the wear monitoring accuracy is improved, and early wear is accurately recognized; machining parameters are intelligently compensated and optimized, precision is improved, and the service life of a tool is prolonged; closed-loop control and multiple technologies are fused, the response time is shortened, and shutdown is reduced; the operation efficiency and reliability of the numerical control machine tool are improved, and the cost is reduced.

The invention discloses a lung cancer lifetime prediction system based on prognosis factor multi-data fusion, and belongs to the technical field of lung cancer prognosis prediction, and the system comprises a multi-source data collection module which is used for collecting prognosis multi-source data of a patient; the multi-source data processing module is used for carrying out preprocessing and feature extraction on the prognosis multi-source data of the patient; the multi-source data fusion module is used for carrying out cross-modal alignment and fine-grained fusion on the extracted multi-modal feature vectors, and capturing a dependency relationship between modals based on a hierarchical attention mechanism to form patient prognosis fusion data; and the survival analysis and prediction module is used for analyzing the prognosis fusion data of the patient according to the lung cancer lifetime prediction model, automatically predicting the lifetime of the patient and displaying the lifetime in a visual form. The problems that existing lung cancer lifetime prediction is low in accuracy and cannot provide support for personalized treatment are solved. The lung cancer lifetime prediction accuracy can be improved, and support can be provided for personalized treatment.

The invention discloses a lung squamous cell carcinoma survival prediction method and system based on SHAP interpretability, and relates to the technical field of medical data analysis. Comprising the following steps: constructing a dynamically updated physiological feature information table based on a lung squamous cell carcinoma clinical data set of a target under multiple time nodes; and associating different survival analysis models with the physiological feature information table to predict the survival probability of the lung squamous cell carcinoma in the target body, the marginal contribution of each input feature to the output of each model is obtained by using the SHAP interpretation algorithm, and the feature uniqueness score is transparently displayed in the fusion process, so that the survival probability of the lung squamous cell carcinoma in the target body can be predicted in the actual use process. The method can visually understand how each feature affects the final prediction, and achieves automatic arbitration or manual recheck through providing a model to explain conflict measurement, invalid variable elimination and pseudo high risk verification and weighted scoring, thereby guaranteeing the reasonability and safety of prediction and intervention suggestions, reducing the decision risk, and enhancing the clinical trust.

The invention belongs to the technical field of medical image processing, and particularly relates to an esophageal cancer treatment effect and survival combined prediction method and system based on multiple modalities, and the method comprises the steps: obtaining a preoperative CT image and Hamp of an esophageal squamous cell carcinoma patient; e, the dyed digital pathological image, transcriptome data and clinical diagnosis and treatment information are preprocessed and subjected to feature extraction, and radiomics embedding representation, pathomics embedding representation and pipeline branch embedding representation are obtained respectively; the radiomics embedded representation, the pathomics embedded representation and the pipeline branch embedded representation are aligned and input into a multi-modal fusion module based on a multi-head self-attention mechanism for feature fusion, and fusion feature representation is generated; and based on the fusion feature representation, synchronously outputting a curative effect prediction result and a survival prediction result by using a multi-task output module, and evaluating model prediction performance by using a curative effect evaluation index and a survival analysis index.

The invention discloses a cerebral hemorrhage postoperative gastrointestinal hemorrhage prediction method based on an LGBM model. The method comprises the steps of obtaining a multi-dimensional clinical feature sequence of a target patient, screening out a stable feature subset by adopting a Boruta feature selection algorithm, performing nonlinear relation fitting and integrated decision by utilizing a pre-trained LightGBM machine learning model, and generating an individualized ATH risk probability value; and when the risk probability value exceeds a dynamic risk threshold value, triggering a high-risk early warning signal, and based on a Kaplan-Meier survival analysis model, carrying out association mapping on a prognosis track of poor long-term neural function recovery, and finally generating a comprehensive prediction report. According to the invention, accurate quantitative evaluation of ATH risk is realized, clinical intervention timeliness is improved through a dynamic threshold early warning mechanism, short-term complication risk and long-term function prognosis are organically combined, and a comprehensive and reliable prognosis basis is provided for individualized treatment decision.

The invention discloses a multi-mode head and neck tumor segmentation and survival prognosis prediction method. The method comprises the following steps: constructing a DE-LS-UNet model; training the DE-LS-UNet model to obtain a trained DE-LS-UNet model, and inputting the new PET image and the CT image into the trained DE-LS-UNet model to obtain a predicted tumor segmentation mask; extracting multi-modal radiomics features according to the predicted tumor segmentation mask, and fusing clinical features through the multi-modal radiomics features to generate multi-source features of the patient; inputting the multi-source features of the patient into the survival analysis model for training to obtain a trained survival analysis model, and inputting the features of the patient into the trained survival analysis model to obtain a prognosis prediction result. According to the method, a feature extraction and fusion mechanism is provided, and a survival analysis model strategy is combined, so that the stability and prediction performance of survival prognosis modeling can be enhanced while the image segmentation precision is improved.

The invention discloses a stomach cancer postoperative survival prediction model construction and verification method based on machine learning, and belongs to the technical field of medical and industrial combination. According to the technical scheme, the method comprises the steps of obtaining clinical data of postoperative patients of gastric cancer, preprocessing the clinical data, and dividing the data into a training set and a verification set; screening out independent risk factors through single-factor and multi-factor Cox proportional risk regression analysis; constructing and training a plurality of prediction models based on the independent risk factors; the model is evaluated through time dependence AUC, C-index, a calibration curve and Kaplan-Meier survival analysis; and selecting the model with the optimal performance as a final prediction model. The method has the beneficial effects that the constructed GBM prediction model remarkably improves the gastric cancer postoperative survival prediction accuracy, the model can effectively process a complex nonlinear relationship in clinical data, a clear and explainable prediction basis is provided by using the SHAP value, and the transparency and clinical credibility of the model are enhanced.

The invention discloses a periodontitis dynamic prognosis prediction model construction method and system based on survival analysis, and the method comprises the steps: collecting and preprocessing periodontitis patient data, and constructing a structured multi-modal clinical data set; the method comprises the following steps: randomly dividing a multi-modal clinical data set into a training set and a test set according to patient IDs, only on the training set, carrying out single-factor Cox proportional risk regression analysis on all patient level data and tooth level data together, and screening indexes significantly related to periodontitis progress risks; taking periodontitis progress time as a dependent variable, and using the training set and the screened indexes to construct a time-dependent Cox periodontitis dynamic prognosis prediction model; and introducing an SHAP algorithm to analyze a prediction result of the time-dependent Cox periodontitis dynamic prognosis prediction model. The method can be used for realizing dynamic, accurate and explainable prediction of the periodontitis progress risk.

The invention discloses an IgA nephropathy survival analysis method and system based on weak supervised learning, and belongs to the technical field of medical care information, and the method comprises the steps: extracting a label and a probability of a second region of interest from a full-slice image through a first model; obtaining a fusion feature group according to the label and the probability; and analyzing the fused feature group through a prediction model to obtain a survival analysis result. A second region of interest of the full-slice image subjected to PAS dyeing is identified by using cooperation of a MoE-based segmentation model and a gating network, and a label is established for the second region of interest through a weak supervision method, so that a high-risk region can be identified; and establishing a fusion feature group according to the probability and the label of the second region of interest identified by the first model, constructing an analysis model on the basis of the fusion feature group, and carrying out survival analysis on the IgA nephropathy, so that the analysis efficiency is improved.

The present application belongs to the technical field of user operation, and specifically relates to a user operation method based on a survival analysis model, which comprises the following steps: S1, data definition and mathematical modeling; S2, descriptive analysis of user survival characteristics; S3, comparative analysis of the differences of different user groups; S4, modeling and prediction of user behavior influencing factors; and S5, application of analysis results in operation decision-making. The present application can realize the dynamicization and refinement of user insight, provide objective and quantitative user stratification standards, empower precise operation and resource optimization, and improve the effectiveness of user loss early warning.

The application discloses a lumbar disc herniation postoperative recurrence risk prediction system based on domain offset correction, and relates to the technical field of medical data processing and clinical disease risk prediction. The system obtains the multi-modal clinical and imaging feature vectors and follow-up outcome data of a training queue and an independent verification queue, trains a central classifier to quantize and correct the multi-center data distribution difference, calculates and trims the importance weight of each training case, trains a survival analysis base learner based on the processed weight and performs stacked integration, determines a final deployment model in combination with an independent verification case queue, calls the final deployment model to perform reasoning on a target patient after lumbar disc herniation surgery, and outputs an individualized recurrence risk score, risk stratification and recurrence-free survival curve, thereby improving the robustness and reliability of the risk prediction system in a cross-center scenario, and being suitable for postoperative follow-up management of lumbar disc herniation and auxiliary clinical decision-making.

The invention discloses a deep learning-based lung cancer adjuvant chemotherapy benefited patient identification method and system, and relates to the technical field of lung cancer precise treatment and artificial intelligence crossing, and the method comprises the steps: analyzing and preprocessing preoperative and postoperative registration logs of a patient to obtain basic information of the patient, a pathological image of the patient and a treatment strategy of the patient; the method comprises the following steps: performing segmentation processing on a patient pathological image by adopting nnformer to obtain a focus region segmented image, performing feature extraction and enhancement on the focus region segmented image to obtain image omics features, and constructing an improved 3D survival analysis model; and analyzing the basic information of the patient, the radiomics characteristics and the treatment strategy of the patient by adopting an improved 3D survival analysis model to obtain an identification result of the lung cancer adjuvant chemotherapy benefited patient. According to the method, the quantitative survival risk score and the adjuvant chemotherapy sensitivity conclusion are output, and an interpretable reference basis is provided for clinical decision making.

The application provides a kind of based on the reliability evaluation method of locking release mechanism of dynamics, including the randomness modeling of the initial position of stud in locking release mechanism;Degradation characteristic modeling is carried out for the piezoelectric coefficient degradation of piezoelectric stack;The randomness of stud and the degradation characteristic of piezoelectric stack are introduced into the dynamics model of locking release mechanism;The response of mechanism under different conditions is analyzed by numerical simulation;The failure mode of locking release mechanism is defined in combination with simulation results;The reliability of locking release mechanism is evaluated by using Kaplan-Meier survival analysis method.The application improves the accuracy and effectiveness of reliability evaluation, accurately evaluates the dynamic response of locking release mechanism under different working conditions, more accurately predicts the possible failure mode and failure probability of mechanism after long time operation;It can better simulate and evaluate the influence of these complex environments on system reliability to ensure stability and safety in actual tasks.

The invention relates to the technical field of machine learning medical prediction, in particular to a diabetes-related pancreatic cancer risk prediction method based on a machine learning model and biological age, and the method comprises the steps: constructing a health reference population queue and a type 2 diabetes application verification queue; determining a core index panel through an automatic machine learning process, and training by adopting a regularization survival analysis model to obtain biological age and age acceleration; based on multi-modal features such as age acceleration, predicting a future pancreatic cancer absolute risk probability by using a machine learning competitive risk model, performing risk grade division, calculating an equal-risk age and supporting risk trajectory simulation according to the future pancreatic cancer absolute risk probability; and packaging the model into a risk prediction toolkit with an adaptive calibration function, and outputting a comprehensive risk assessment report. According to the method, the biological age is trained by adopting the pure health queue, so that the interference of the disease state on aging measurement is avoided, and the prediction precision of the pancreatic cancer risk of the type 2 diabetes mellitus population is improved.

The present invention belongs to the technical field of bioinformatics, and relates to a machine-learning-based method for screening cross-differentially expressed genes between lung cancer and COVID-19 infection and correspondingly screening prognostic genes of lung cancer. The method comprises: acquiring cross-differentially expressed genes between lung cancer and COVID-19; screening the cross-differentially expressed genes between lung cancer and COVID-19 infection; using Cox regression and LASSO regression to correspondingly screen prognostic genes of lung cancer; and using a K-M survival analysis method, GO and KEGG enrichment analysis methods and a protein-protein interaction analysis method to analyze screened-out prognostic genes of lung cancer. The method provided herein can process data, and can also identify intricate patterns in the data, and has relatively high sensitivity and specificity, thereby improving the efficiency and accuracy of screening. The technique can be extended to the research of other diseases, and features universality.

The invention discloses a breast cancer heterogeneity analysis system based on unicellular omics and Mendel randomization, and relates to the technical field of biological information. Comprising a data acquisition module, a single cell data processing module, a differential expression analysis module, a Mendel randomization analysis module, a survival analysis module, a function enrichment module, a drug prediction module and an output module. The system is used for integrating multiple omics data and identifying causal driving genes in breast cancer malignant epithelial cell subgroups. According to the system, unicellular omics and Mendel randomization are combined, specific causal inference of breast cancer heterogeneity cell types is achieved, key genes are identified, part of the genes are positively or negatively correlated with risks, and clinical significance is verified through survival analysis; the system provides new biomarkers and targets for accurate treatment of breast cancer, and has the advantages of high precision, strong repeatability and large clinical transformation potential.

The invention relates to the technical field of stroke monitoring, and discloses a method and a system for detecting the stroke risk after waking of a home elder based on water and electricity information, and the method comprises the steps: synchronously obtaining household water, electricity and body data in a morning waking monitoring time period, and generating a morning waking water event sequence and an electricity load sequence through flow sequence form segmentation and electricity event steady-state analysis; morning awakening physiological indexes are formed according to the skin electricity, the heart rate and other data; determining a unified time reference according to a morning beginning behavior completion moment or a physiological composite sign, constructing an alignment time window, and mapping the three types of data to a discrete time grid; establishing double-hypothesis probability inference, respectively constructing water utilization, power utilization and body three-channel observation likelihoods, and performing joint fusion with a dependency relationship to obtain a current occurrence probability; and establishing a survival analysis model by using the time variation covariable in the aligned time window, and giving the occurrence probability of the future time period. The method does not depend on videos, and the judgment capability can be kept when the body data are missing.

The present application belongs to the technical field of biomarkers, and particularly relates to a breast cancer biomarker and application thereof. The present application first identifies long-chain non-coding RNA CCLA, and confirms that the expression of CCLA in triple-negative breast cancer tissue is significantly higher than that in normal tissue, and high expression is closely related to poor prognosis of patients. Through RT-qPCR verification and survival analysis of 81 TNBC clinical samples, the potential of CCLA as a specific biomarker of TNBC is confirmed, a new specific target for precise diagnosis and treatment of TNBC is provided, and the shortage of TNBC target points in the prior art is effectively overcome.

The application discloses a kind of wind turbine power characteristics evaluation method based on cloud model and optimal combination weighting based on survival analysis intelligent electric energy meter reliability evaluation method, step S101, electric energy meter original data acquisition and category characteristic numerical processing;Step S102, category characteristics are reduced dimension processing;Step S103, evaluate batch intelligent electric energy meter reliability;Step S104, evaluate the reliability of individual intelligent electric energy meter;The method can realize the reliability analysis of batch intelligent electric energy meter, and the survival rate of each individual intelligent electric energy meter is evaluated, which helps electric power department to find the abnormal condition of intelligent electric energy meter in time, provides theoretical support for the transformation of intelligent electric energy meter from "expiration rotation" to "state replacement", and improves the lean operation and maintenance management level of intelligent electric energy meter.

This invention discloses a system for predicting the risk of recurrence after lumbar disc herniation surgery based on domain offset correction, relating to the fields of medical data processing and clinical disease risk prediction technology. The system acquires multimodal clinical and imaging feature vectors and follow-up outcome data from training and independent validation cohorts. It trains a center classifier to quantify and correct for differences in multi-center data distribution, calculates and normalizes the importance weights of each training case, trains a survival analysis base learner based on the processed weights, and performs stacking and ensemble integration. The final deployment model is determined by combining the independent validation case cohort. The final deployment model is then used to infer the risk of recurrence in target patients after lumbar disc herniation surgery, outputting individualized recurrence risk scores, risk stratification, and recurrence-free survival curves. This improves the robustness and reliability of the risk prediction system in cross-center scenarios and is suitable for follow-up management and clinical decision support after lumbar disc herniation surgery.

The present application relates to the cross field of intelligent transportation, human factors engineering, emergency management and artificial intelligence, and particularly relates to a fire driving fatigue monitoring system and method based on survival analysis, which fuses multi-modal data such as physiology, behavior, environment and task, constructs a time-dependent dynamic risk assessment model, introduces a survival analysis framework to quantitatively determine the probability of accidents caused by fatigue during task execution, and identifies fatigue critical points with intervention value in combination with an adaptive threshold mechanism, thereby providing scientific rest determination and takeover decision basis for the task management system. The present application solves the problems of existing fatigue recognition technology in special operation scenarios, such as hysteresis, limitations and lack of critical risk judgment ability; it takes into account real-time, predictability and deployability, and is suitable for high-risk driving environments such as fire fighting, police and dangerous chemical transportation.

The application discloses an intelligent heat boiler equipment monitoring control system and relates to the technical field of monitoring control, solves the technical problems that it is difficult to identify the equipment operation state, analyze noise source positioning and noise pollution degree value, construct a self-attention mechanism combined with a survival analysis model to predict fault probability, analyze various monitoring data to comprehensively evaluate a risk state index, dynamically allocate recovered heat and generate a closed-loop feedback mechanism through a digital twin verification platform, and the like, and the application realizes comprehensive monitoring of heat boiler equipment, comprehensive evaluation of a risk state index, optimization of heat distribution, remote intelligent control, improvement of equipment operation efficiency and safety through the combination of various modules.

The invention discloses a solar flare prediction model construction method based on intelligent survival analysis support, and the method comprises the steps: 1, defining main parameters in survival analysis in combination with a solar flare prediction scene; step 2, carrying out data preprocessing on the solar flare observation data; step 3, constructing a hybrid model combining LSTM (Long Short Term Memory) and DeepSurv (DeepSurv); step 4, training of a hybrid model combining LSTM and DeepSurv is carried out; and step 5, performing prediction and visualization after model training. By using the method of the invention, a time sequence characteristic and survival analysis fused extensible network can be constructed, solar flare prediction performance can be evaluated, and outbreak time prediction understanding can be assisted.

The application provides a tea garden risk prediction method and system based on multi-modal data, comprising: based on risk correlation constraints, high-dimensional survival analysis and contribution degree filtering and noise removal are performed on a tea garden multi-modal data set, and space-time feature parallel extraction is performed, to obtain a multi-modal space-time feature set; based on a dynamic causal discovery algorithm, loop-free causal constraints are performed on the multi-modal space-time feature set, to obtain a modal causal graph; a time delay response kernel matrix is extracted from a lag causal matrix of the modal causal graph, the time delay response kernel matrix is taken as a prior constraint, cross-modal feature fusion is performed on the multi-modal space-time feature set based on a causal attention mechanism, to obtain a fused multi-modal feature; based on a hybrid expert architecture, multi-task risk analysis is performed on the fused multi-modal feature, to obtain a comprehensive risk vector; contribution degree attribution is performed on the comprehensive risk vector, to obtain a contribution heat map, cross verification is performed on the comprehensive risk vector in combination with the modal causal graph, and a tea garden risk report is generated.

The invention relates to the technical field of intelligent traffic and itinerary planning, in particular to an intelligent risk insight system based on flight event flow prediction and itinerary re-planning. Comprising a journey planning unit, a flight event flow prediction and journey planning unit and a client. The route planning unit fuses real-time traffic and airport operation data, and provides route planning and time estimation before boarding for users. The core flight event flow prediction and route planning unit is used for predicting flight time probability distribution by utilizing a quantile regression model through multi-source data fusion and real-time feature engineering, predicting a next key event by utilizing a survival analysis model, and carrying out quantification and interpretable attribution on risks; an intelligent re-planning scheme with a trigger condition is generated based on multi-objective optimization; and the final result is encapsulated and then presented through the client. According to the invention, active perception, accurate prediction and intelligent decision-making assistance of flight trip full-chain risks are realized.

The invention provides a user viscosity analysis management method and system. The method comprises the following steps: S1, carrying out multi-source data integration; s2, carrying out real-time data stream processing; s3, a step of carrying out advanced feature extraction; s4, carrying out dynamic clustering analysis; s5, a step of constructing a viscosity prediction model; s6, generating a personalized intervention strategy; s7, implementing an A / B test framework; s8, carrying out real-time feedback circulation; s9, carrying out long-term trend analysis; and S10, carrying out system automation and optimization. The user viscosity analysis and management method has the following advantages that the prediction accuracy is improved, the user loss risk is predicted more accurately through a complex statistical model (such as Cox survival analysis) and advanced feature engineering, and misinformation and missing report are reduced.

The invention relates to the technical field of digital pathology and artificial intelligence, in particular to an immunohistochemical curative effect prediction system based on dynamic analysis, and the system comprises an image processing module which is used for obtaining and preprocessing paired immunohistochemical digital pathological sections before and after the treatment of the same patient; the dynamic registration and analysis module is used for performing high-precision image registration on the paired slices and extracting dynamic quantization parameters of biomarker expression changing along with treatment; the spatial heterogeneity quantification module is used for dividing tumor function sub-regions and quantifying spatial distribution heterogeneity parameters of the biomarkers in different function sub-regions; and the fusion prediction module is used for carrying out feature fusion on the dynamic quantization parameters and the spatial distribution heterogeneity parameters, and outputting a clinical curative effect prediction result through a survival analysis model. According to the system, by performing dynamic and quantitative analysis on paired immunohistochemical slices before and after treatment and integrating multi-modal data, accurate prediction of the tumor immunotherapy curative effect is realized.

The invention discloses a multi-modal survival analysis method and device based on a hypergraph network and knowledge distillation, and relates to the technical field of bioinformatics and medical image processing. Comprising the following steps: acquiring a pathological full-slice image of a target patient and multi-omics gene data to construct a training data set, and constructing a multi-modal hypergraph survival analysis network containing a feature extraction and compression module, a hypergraph fusion module and a graph distillation module. The feature extraction module processes the image by using a prototype attention mechanism to generate alignment features; the hypergraph fusion module constructs a hypergraph structure and outputs fusion features; the graph distillation module constructs a graph distillation mechanism. And iteratively executing a model training step, training the network by using the training data set, and constructing a total loss function optimization parameter. And outputting a survival risk prediction result of the to-be-processed patient. The method solves the problems that an existing method is difficult to capture complex high-order correlation among different modes, semantic gaps exist in feature space due to mode heterogeneity, survival risk prediction results are inaccurate, and applicability is limited.

The application discloses an analysis method, system and device based on cancer cross-modal data and a medium, and comprises the following steps: in response to receiving an analysis request input by a user, inputting the analysis request to a first large model, determining an analysis task and an analysis tool for executing the analysis task; in the case where the analysis task comprises a target analysis task, extracting information from clinical data input by the user by using an information extraction engine to obtain object basic data; obtaining a gene expression matrix formed by cancer transcriptome data after dimension reduction based on the object basic data by using a wisdom algorithm contraction engine; performing survival analysis on the gene expression matrix after dimension reduction by using a risk mapping engine to obtain a survival analysis result and a target gene set affecting survival; and checking the target gene set based on reference data by using an arbitration evaluation engine in the analysis tool to obtain a checking result.