47 results about "Survival data" patented technology

The invention relates to an intelligent aeronautical data recording instrument, which is characterized in that a main control MCU adopts a dynamic algorithm processing system to analyze and compress the aeronautical flight data which is collected by a flight data collection system and adopts a streaming media dynamic processing system to analyze and filter voice information which is collected by a voice collection system, and the aeronautical flight data and the voice information after being processed are transmitted into an internal memory card and a crash survival data protection system. The present aeronautical data recording instrument is upgraded to three data processing system, and a data backup system is added, so the possibility for searching the aeronautical data recording instrument after the airplane is crashed is further improved, and the service life of the memory hardware is prolonged, the working efficiency is improved; different feasible and reliable technical reports and market analysis reports are supplied for the maintenance of the airplane and the development of the enterprise by comprehensively managing the flight data through a local ground data center; and chip structure is adopted, so the size is greatly reduced, and the memory space is multiplied.

The invention discloses an esophageal squamous carcinoma radical postoperative patient prognosis prediction model construction method and device, and the method comprises the steps: obtaining clinical diagnosis and treatment data and follow-up visit survival data, carrying out multi-factor Cox regression analysis on patient characteristic variables, tumor pathology characteristic variables, treatment condition variables and test index variables according to follow-up visit survival data, carrying out variable screening by utilizing a step-by-step back algorithm and an Akaike information criterion, and carrying out variable screening on the screened candidate variables again to obtain modeling variables; and performing multi-factor Cox regression analysis on modeling variables and interaction items of every two modeling variables to construct a prognosis prediction model of a patient after the esophageal squamous carcinoma radical operation, wherein the prediction variables comprise age, gender, tumor primary position, T stage, lymph node detection number, tumor size, preoperative hemoglobin level and N stage treatment mode interaction items. According to the method, the prediction accuracy can be improved, the optimal benefit group of different treatment schemes is defined, and the prognosis evaluation precision of the esophageal squamous cell carcinoma is realized.

The invention provides a system and a method for predicting tumor mutation load of a patient with advanced lung cancer. The system comprises a data acquisition module for acquiring a first lung tomography image and a real tumor mutation load of an early lung cancer patient, and a second lung tomography image and clinical survival data of an advanced lung cancer patient; a first processing module used for labeling by adopting a preset labeling box to obtain a first lung labeling image and a second lung labeling image; adding each first lung labeling image and the real tumor mutation load into afirst data set; a model training module used for training according to the first data set to obtain a tumor mutation load prediction model; a second processing module used for inputting each second lung labeling image into the tumor mutation load prediction model to obtain a predicted tumor mutation load; and performing survival analysis according to the clinical survival data to obtain a survival analysis curve, and processing to obtain the similarity between the distribution curve for predicting the tumor mutation load and the survival analysis curve. The prediction accuracy is effectivelyimproved; and the cost is saved.

The subject disclosure presents systems and computer-implemented methods for providing reliable risk stratification for early-stage cancer patients by predicting a recurrence risk of the patient and to categorize the patient into a high or low risk group. A series of slides depicting serial sections of cancerous tissue are automatically analyzed by a digital pathology system, a score for the sections is calculated, and a Cox proportional hazards regression model is used to stratify the patient into a low or high risk group. The Cox proportional hazards regression model may be used to determine a whole-slide scoring algorithm based on training data comprising survival data for a plurality of patients and their respective tissue sections. The coefficients may differ based on different types of image analysis operations applied to either whole-tumor regions or specified regions within a slide.

A system and method for retaining routes in a control plane learned by an inter-domain routing protocol in the event of a connectivity failure between routers. Routers are classified as either route reflectors or originators. A connectivity failure between two routers is determined. A determination is then made whether to propagate a withdrawal of learned routes based on whether the connectivity failure occurred between a route reflector and an originator, two originators, or two route reflectors. A withdrawal of learned routes is propagated to neighboring routers if the connectivity failure occurred between two originators, or between a route reflector and an originator that is inaccessible via an intra-domain routing protocol. No withdrawal of learned routes is propagated if the connectivity failure occurred between two route reflectors, or between a route reflector and an originator that is accessible via an intra-domain routing protocol.

The subject disclosure presents systems and computer-implemented methods for assessing a risk of cancer recurrence in a patient based on a holistic integration of large amounts of prognostic information for said patient into a single comparative prognostic dataset. A risk classification system may be trained using the large amounts of information from a cohort of training slides from several patients, along with survival data for said patients. For example, a machine-learning-based binary classifier in the risk classification system may be trained using a set of granular image features computed from a plurality of slides corresponding to several cancer patients whose survival information is known and input into the system. The trained classifier may be used to classify image features from one or more test patients into a low-risk or high-risk group.

The invention discloses a data collection method and system, and relates to the field of computer network technology. Via dynamical monitoring, survival data processing centers and collection probes are dynamically searched to implement corresponding collection when a data processing center is disconnected from a collection probe, and the problem that the whole data collection task cannot be carried out correctly due to stop of the collection probe or data processing center for certain reason is solved. Via load balancing, multiple collection probes can be used to implement collection when multiple devices are collected in a batch manner, and compared with a single collection probe in the prior art, the efficiency can be improved. Compared with a single-collection-probe mechanism in the prior art, collection is prevented from stop caused by that the collection probe is unavailable due to the network and the like.

The invention discloses a postoperative recurrence risk prediction system for patients with stage I lung adenocarcinoma and its application. The postoperative recurrence risk prediction system for patients with stage I lung adenocarcinoma provided by the present invention includes a system for detecting the expression levels of ACADM gene and RPS8 in tumor samples of patients with stage I lung adenocarcinoma. In the above system, the system for detecting the expression levels of the ACADM gene and RPS8 gene in tumor samples from patients with stage I lung adenocarcinoma includes reagents and / or instruments required for detecting the expression levels of the two genes ACADM and RPS8. The present invention conducts gene enrichment analysis on the gene expression profile of patients with stage I lung adenocarcinoma based on the recurrence status, and finds that the risk of postoperative recurrence is related to the expression of ACADM and RPS8 genes in tumor metabolism. The present invention integrates the recurrence-free survival data of 317 patients with stage I lung adenocarcinoma of three independent cohorts, establishes and verifies the individualized RAMS model of stage I LUAD patients.

The invention discloses a double-gene combination for identifying a gastric cancer high-risk immune overdrive subgroup. The double-gene combination is prepared by the following steps: acquiring a gastric cancer immune checkpoint related double-gene combination by utilizing a gastric cancer patient tumor tissue transcriptome and survival data in a public database; detecting expression of double genes in a queue of gastric cancer patients to be detected; identifying respective most significant survival grouping points of the double genes according to the expression of the double genes and the survival data of the queue of the gastric cancer patients to be detected; and according to respective most significant survival grouping points of the double genes, dividing high-risk immune over-driven subgroups of the queue of the gastric cancer patients to be detected. The patient of the high-risk immune over-driven subgroups, identified according to the invention not only has obvious poor prognosis, but also has relatively high pre-stored immunocompetence and immune checkpoint abundance. For the queue of the gastric cancer patients, a patient group suitable for immunotherapy can be identified.

The invention discloses a method for evaluating the individualized prognosis of glioblastoma based on a multi-gene expression characteristic profile, comprising the following steps: obtaining a list of prognostic risk genes and gene weights of glioblastoma; using the tumor of a patient with glioblastoma The tissue transcriptome and survival data were used to construct a prognostic evaluation model; the patient's risk score was calculated according to the gene expression profile of the tumor tissue of the glioblastoma patient; the patient's annual survival probability was calculated according to the patient's risk score. The annual survival probability of glioblastoma patients obtained by the method of the present invention is highly consistent with the actual annual survival rate (linear correlation R 2 = 0.963, P-value = 1.38E-13). It is confirmed that the method has high prediction accuracy, which is highly consistent with the actual survival status. At the same time, for each tumor patient, the present invention can provide the patient-specific survival probability curve.

The invention provides a multi-output gradient boosting tree modeling method for survival risk analysis. The method includes: firstly, under the model algorithm framework of the optimal gradient boosting tree (XGBoost), construct a , the expression of the survival data of the survival prediction model of the transportation or industrial target industry; then define and calculate the loss function corresponding to the survival data; then define and calculate the first-order gradient and second-order gradient corresponding to the loss function; finally calculate The obtained loss function value and the first-order gradient and second-order gradient value of the loss function are simultaneously input into the XGBoos model algorithm framework, and automatically trained to generate the survival prediction model of the target industry. The modeling method of the invention can better represent the relationship between model covariates and risk prediction values; improve the predictive performance and generalization ability of the model; have better predictive performance and risk discrimination; and be applicable to a wide range of scenarios.