5 results about "Survival data" patented technology

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 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.

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.