14 results about "Survival modeling" patented technology

The invention provides a children nephroblastoma machine learning survival model developed based on a center queue, and belongs to the technical field of nephroblastoma prediction and evaluation. And analyzing and evaluating prognosis risk factors of the nephroblastoma, establishing an interpretable machine learning survival model based on the central queue, and finally, converting the model into a clinically friendly online prediction platform to promote the transformation of a prognosis evaluation tool from scientific research to clinical practice, so as to improve the prognosis evaluation efficiency. The method aims at providing dynamic risk assessment and individualized treatment decision support for clinicians, and finally the survival income of relapse / refractory child patients is improved.

The invention relates to the technical field of credit risk assessment, in particular to a supplier credit scoring method, which comprises the following steps of: firstly, constructing an evidence tensor and a constraint residual vector through preprocessing and consistency checking to form a supply network hypergraph; introducing causal selection correction and evidence credibility gating into the discrete time survival model with monotone constraint to obtain a baseline hazard rate, and generating an evidence shadow price and baseline hazard projection model through dual update; spectrum standardization propagation and self-excitation modeling are carried out on the hypergraph, scene weights are obtained in combination with optimal transmission, propagation is completed, and incremental hazards are mapped and orthogonalized; and carrying out order-preserving fusion on a hazard rate layer to obtain a final default probability, determining a threshold value according to cost loss, monotonically mapping the threshold value into a supplier credit score, and realizing explainable and auditable risk quantification and decision support consistent with financial cost.

The present invention provides a method and device for evaluating the survivability of unmanned marine systems based on complex networks. The method comprises: A, judging whether all typical survival models A in the survival situation diagram of the current j-layer system are δ Indicates that they all exist in the situation diagram. If they exist, go to B. If they do not exist, record the first stage identification stop layer information as γ layer and go to C. B, according to A δ Perform model recognition and extraction on the survival situation diagram of the j-layer system, extract the recognition results to the j+1 layer, and return A; C, determine whether there is one or several A in the survival situation diagram of the current γ+k layer system δ If it exists, go to D. If it does not exist, record the number of layers increased by the second-stage recognition stop layer γ+k relative to the first-stage stop layer γ as the second-stage recognition layer number information β and go to E. D, according to A δ Perform model recognition and extraction on the survival situation diagram of the γ+k layer system, extract the recognition result to the γ+(k+1) layer, and return it to C; E to obtain the system survivability assessment result W.

The invention provides a method, equipment, medium and program product for constructing a survival model of catheter-related thrombus based on machine learning, and relates to the field of intelligent medical treatment. The method comprises the following steps: obtaining training set samples including any one or more data of sample basic information, catheter types, system medication treatment and tumor sites and result information corresponding to the samples; processing system medication treatment and tumor part data by adopting a factor folding method to obtain processed system medication treatment and tumor part data characteristics; converting the basic information and the catheter type data into numeric features; and inputting the data features, the numerical features and the result information into a machine learning model for training to obtain an SM-CRT model. By collecting catheter insertion data of a tumor patient, a survival model, namely SM-CRT, of catheter-related thrombus is constructed, and the model is a survival model providing risk scores and catheter thrombus risk time distribution.

The invention discloses a drug design optimization and precision medical analysis method and system based on swarm intelligence. According to the method, behaviors of weak meat and strong food groups are simulated, candidate drug molecules or patient pathological models are regarded as agents, and core data of the agents are protected by hiding virtual models; risks such as toxicity and off-target effect are actively identified and avoided through the security defense model; the intelligent agent is guided to escape from the local optimal solution and the invalid research and development path through the escape model; and finally performing fusion and multiple verification on the elite agent through the survival model, and outputting high-quality and low-risk candidate drug molecules or personalized treatment schemes. According to the method, a safe and anti-interference mechanism is embedded in an optimization process, the efficiency and success rate of new drug research and development are remarkably improved, and a powerful analysis tool is provided for precision medical treatment.

The present application relates to a kind of individual level retention intervention planning method of urban rail transit user, the method includes: based on the obtained multi-source user data, for each user i constructs the training sample containing user survival time, censored state and feature vector;RFM multi-task is trained by using training sample fusion depth survival model, for each user i generates user loss risk trajectory and predicted survival time, and calculates the user value and life cycle value of user i at current evaluation time point t, to construct the structured portrait feature of input large language model for each user i;Based on user declarative preference survey data set constructs user preference priori;Structured portrait feature, user preference priori and operation allowed incentive set are input into large language model as hard constraint, for each user i generates optimal retention plan.Compared with prior art, the present application has the advantages of improving the stability, economy and operation availability of retention decision, etc.

The invention discloses a computer AI auxiliary analysis follow-up visit data system and method, and relates to the technical field of data processing, and the system comprises a data acquisition and preprocessing module, a dynamic fusion model construction module, a deep survival analysis module, an expected effect simulation module and a visual report generation and model iteration module. According to the method, multi-modal follow-up visit data and original data are integrated, a dynamic fusion model and a deep survival model are constructed, accurate long-term and short-term prognosis prediction and survival influence factor analysis are achieved, meanwhile, real-time risk monitoring and intervention effect simulation are conducted in combination with streaming computation, a visual visualization report is generated, the model is continuously iterated and optimized, and the real-time risk monitoring and intervention effect simulation is achieved. According to the method, the efficiency, accuracy and individuation level of medical services are remarkably improved, the workload of medical staff is relieved, and meanwhile, the satisfaction degree and credibility of patients can be improved by providing visual and accurate visual reports and individualized treatment schemes.

The invention relates to the technical field of pathological image processing and medical artificial intelligence auxiliary diagnosis, and discloses a colorectal cancer prognosis analysis system and method fusing tissue and block characteristics, and the system comprises an image block cutting and standardization module which generates a standardized image block set under different multiplying power; the ResNet50 classifier module is used for carrying out organization classification on the image blocks; the tissue type specific feature extraction module is used for extracting multi-scale survival correlation features; the tissue spatial feature extraction and quantitative analysis module is used for reconstructing a multi-tissue type distribution diagram and calculating quantitative spatial features such as the number of lymphocytes and the intraperitoneal ratio after morphological repair; and the multi-modal feature fusion and survival prediction module is used for fusing the pathological deep features, the spatial segmentation features and the clinical data, and outputting an individualized prognosis risk score by using a survival model. According to the method, a macrostructure and microcosmic details are effectively integrated, and the accuracy and interpretability of colorectal cancer survival risk layering are improved.

This application provides a method for regulating the activity and distribution of microbial agents used to remediate underground contamination in complex formations. The method includes: collecting microbial agent samples and monitoring parameters such as temperature, humidity, and oxygen levels in real time; using a genetic algorithm to optimize the combination of environmental variables to obtain an activity maintenance threshold; employing a microbial agent survival model to construct a three-dimensional mesh representation based on formation scanning data; using finite element analysis to analyze permeability differences and determine complex formation zones; based on a uniform distribution scheme, using real-time sensor feedback of injection process data to determine distribution path deviation; if the deviation exceeds a preset value, dynamically adjusting the injection point position to obtain a precise penetration trajectory; using the precise penetration trajectory to map the coverage area to the pollution source location; verifying the coverage integrity through Monte Carlo simulation to determine the pollution coverage mesh.

Provided is a method for fraud prevention using deep learning and survival models. The method may include receiving, with at least one processor, transaction data associated with a plurality of transactions of at least one payment account. At least one attempted attack may be detected based on the transaction data. A fraud risk score for each subperiod of a plurality of subperiods in a time period following the at least one attempted attack may be generated based on the transaction data using a deep learning model and a survival model. The fraud risk score for each respective subperiod may be associated with a probability that a fraudulent transaction will not occur by the respective subperiod. A system and computer program product are also disclosed.

A computer-implemented system and method are provided for predicting consequences of obstructive sleep apnea. At least one computing device can access information associated with overnight polysomnography and can extract features from the information. The extracted features are across three physiological domains. Moreover, each of the features can be represented across the three physiological domains as histograms, that are applied to two extreme gradient boosting (XGBoost) models. Moreover, the computing device(s) can predict, as a function of the two XGBoost models, short-term excessive daytime sleepiness (EDS) and long-term all-cause mortality. The histograms can be applied by the at least one computing device to a gradient-boosting survival model and the at least one computing device can evaluate, as a function of the gradient-boosting survival model, survival trajectories, and can evaluate, using artificial intelligence, feature importance of the information associated with overnight polysomnography for predicting a consequence of obstractive sleep apnea.

The application provides a method, device, medium and program product for constructing a survival model of catheter-related thrombosis based on machine learning, and relates to the field of intelligent medical treatment. The method comprises the following steps: acquiring training set samples, which include sample basic information, catheter types, systemic drug treatment, any one or several data in tumor sites and corresponding result information of the samples; processing the data of the systemic drug treatment and the tumor sites by using a factor folding method to obtain processed data characteristics of the systemic drug treatment and the tumor sites; converting the basic information and the catheter type data into numerical features; and inputting the data characteristics, the numerical features and the result information into a machine learning model to obtain an SM-CRT model. The application collects catheter insertion data of tumor patients to construct a survival model of catheter-related thrombosis, namely an SM-CRT model, which is a survival model for providing a risk score and a catheter thrombosis risk time distribution.

The invention discloses a brain glioma patient survival prediction method based on bidirectional hierarchical collaborative attention. The method comprises the following steps: firstly, acquiring a pathological image of a patient and multi-parameter MRI of the pathological image, and performing pathological image feature extraction and radiology image feature extraction; and then two-stage deep fusion is carried out through the first bidirectional hierarchical collaborative attention module and the second bidirectional hierarchical collaborative attention module, and after a final fusion feature is obtained, the final fusion feature is input into a discrete survival model for survival prediction. According to the invention, deep fusion is carried out on the characteristics of the pathological image and the image, bidirectional information is enabled to flow and guide each other through the bidirectional hierarchical collaborative attention module, information loss or incomplete fusion caused by unidirectional information flow is avoided, a complex dependency relationship between the two modals is better captured, information interaction is enhanced, and the accuracy of information fusion is improved. The method greatly improves the precision of information fusion, improves the understanding and prediction capability of the model for different modal images, and improves the precision of survival prediction.

A wireless device detection system includes sensors to receive temporary identifiers transmitted between a base station and a mobile wireless device. A cumulative distribution function for survival probability is generated based on delta times between multiple access events for each temporary identifier. In response to a new access event, a corresponding temporary identifier is added to a streaming list and assigned a survival probability value, based on the cumulative distribution function, to a latest access event for each temporary identifier contained in the streaming list. Temporary identifiers that have a survival probability value less than a threshold value are removed from the streaming list. The number of temporary identifiers contained in the streaming list are compared to a number of devices known to be present. The probability that a device corresponding to each of the temporary identifiers contained in the streaming list is present is determined.