139 results about "Risk classification" patented technology

A method and system of determining cost of automobile insurance based on safety characteristics of road segments comprising routes expected to be followed on trips insured vehicle and driver have made or are expected to make over the time period the insurance contract is in force. The method comprises steps of identifying the trips, selecting routes expected to be followed on the said trips, determining the probability of each selected route being followed on a trip, evaluating the safety characteristics of roads comprising the routes, consolidating the route safety information with the assigned route probabilities and other risk classifications to produce cost of insurance for the identified trips. The results are further consolidated with the cost of insurance due to risk exposures other than those of the identified trips to produce a final insurance cost.

Genes and gene expression profiles useful for predicting outcome, risk classification, cytogenetics and / or etiology in pediatric acute lymphoblastic leukemia (ALL). OPAL1 is a novel gene associated with outcome and, along with other newly identified genes, represent a novel therapeutic targets.

The invention discloses a hybrid machine learning credit scoring model building method. The method comprises the steps of 1, determining client risk classification standards based on a loan client historical data set; 2, based on the loan client historical data set, obtaining a loan client data feature set through feature extraction; 3, selecting at least two model algorithms from an alternative model library, building corresponding models based on the selected algorithms, performing model performance check on the built models by adopting a K-fold cross check method, performing standard check on the models about to pass the model performance check based on model check standards, obtaining evaluation index statistical quantity values, and selecting a model type used by final modeling according to the evaluation index statistical quantity values returned by the standard check of the models; and 4, based on the algorithm corresponding to the selected model type, building a credit scoring model. The technical effect of efficiently and accurately finishing user credit evaluation through the built hybrid machine learning credit scoring model is achieved.

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.

A method and system for administering a disease management program to improve healthcare quality, reduce healthcare costs, and optimize delivery of healthcare services. A multi-condition risk assessment is conducted for all or a substantial portion of a population of program participants, and collected multi-condition risk assessment data are combined with claims data for predictive modeling of future healthcare risk and expense. Participants are risk-stratified into one or more classifications of future healthcare cost risk, and appropriate intervention or delivery of healthcare services is made based on the risk classification.

Genes and gene expression profiles useful for predicting outcome, risk classification, cytogenetics and / or etiology in pediatric acute lymphoblastic leukemia (ALL). OPAL1 is a novel gene associated with outcome and, along with other newly identified genes, represent a novel therapeutic targets.

A medical risk assessment method and computer program product resident on a computer or a hand-held device that allows a physician to determine the best strategy for primary and secondary cardiovascular disease prevention utilizing current guidelines and published medical literature. The computer program product evaluates a number of risk factors to determine specific recommendations for an individual patient, including Framingham risk scoring (FRS), pertinent medical history, individual lipid panel and advanced lipoprotein profiling, patient laboratory test results, and published literature on the effects of anti-lipid medicines on plasma concentration and / or composition of lipoprotein molecules and clinical outcomes. The risk assessment method establishes a cardiovascular treatment therapy strategy for a patient by determining a cardiac risk classification group, determining a cardiovascular treatment therapy based on the patient's lipoprotein profile and the patient's cardiac group risk classification, and presenting the cardiovascular treatment therapy for the patient to a medical practitioner on a patient evaluation display.

The embodiment of the invention provides a collection method and device based on user data and a storage medium. The method comprises the steps of determining a target user whose repayment day entersan appointed deadline; obtaining user data of the target user, wherein the user data comprises user attribute data and user behavior data related to the collection service; generating a feature vectorof the user data; Inputting the feature vector into a preset collection risk classification model, and determining a collection risk category of the target user; and determining a collection strategycorresponding to the collection category of the target user according to a preset corresponding relation between the collection risk category and the collection strategy. The embodiment of the invention can improve the collection success rate, and is high in efficiency and low in cost.

A full-service turn-key in-application commerce solution with fraud detection is disclosed that provides web service interfaces to a commerce system. The in-application solution features fraud detection with user behavior tracking and fraud controls that limit the features that are offered to a user. Fraud detection involves input from the application, the commerce system, or third party systems. User fraud scores are updated frequently as events are processed. Controls are applied to the user account based on the user fraud score and risk classifications for ranges of fraud scores. An in-application solution also features a wallet with a personal threshold for microtransaction spending, limiting the amount that the user is allowed to spend before a wallet is cleared. The wallet is integrated with the fraud detection such that the personal threshold is determined from the user's latest fraud score.

The invention discloses a financial risk control system based on big data and artificial intelligence. The system include a risk information collection module, an information management module, a riskassessment module, a risk processing module, and a risk storage module. A risk information management server comprises a risk classification unit, a risk identification unit and a key information extraction unit. A risk assessment server comprises a risk management unit, an assessment unit and a risk pre-storage unit. The risk processing module comprises a processing node, a statistical unit anda generation unit. The system classifies risk items in the risk management server by keyword extraction and comparison methods, and at the same time, the classified risk items are assessed in the risk assessment server, so as to improve risk management and control efficiency. The system can automatically control risk in real time, thereby preventing occurrence of major risk items, and the systemcan improve and update risk control rules, so as to make risk control of a transaction process more comprehensive and save human resources.

Methods and systems are presented for dynamically adjusting a risk classification of a risk source based on classifications of one or more other risk sources. The risk engine may first classify a first risk source as a first risk type based on an initial analysis of the first risk source. Subsequent to classifying the first risk source as the first risk type, the risk engine may determine that a second risk source is associated with a second risk type. Based on the determination that the second risk source is associated with the second risk type, the risk engine may re-classify the first risk source as the second risk type. The risk engine may then use the reclassification of the first risk source to improve network security of an online service provider.

An environment risk sorting system includes a plant environment database for storing pieces of environment risk data; a risk sorting server for updating the environment risk data; a managing server coupled with the risk sorting server, wherein, the risk sorting server executes generating through a first risk evaluation module, based on the environment risk data, a first risk evaluation result for each abandoned plant, which produces an environmental site evaluation list, through which, an environmental site evaluation data of each abandoned plant and a on-spot environmental site evaluation data are obtained, updating through a second risk evaluation module succeeding environment risk data based on the environmental site evaluation data of each abandoned plant and hence generating a second risk evaluation result for each abandoned plant, which, in turn, produces an investigation list of each abandoned plant as a basis for follow-up investigation.

The invention discloses a risk ranking control method of shield penetrating through buildings at the soft soil stratum, and belongs to the technical field of shield engineering risk identification. The risk ranking control method comprises the steps that factors affecting building safety are analyzed through data collection, main influence factors of shield penetrating through the buildings are subjected to risk classification study, risks of shield penetrating through the buildings are primarily classified, correction influence factors of shield penetrating through the buildings are subjected to the risk classification study, the risk primary classification of shield penetrating through the buildings is corrected, and corresponding control measures are carried out on different risk ranks. According to the method, risk factors influencing shield penetrating through the buildings are effectively and reasonably analyzed and identified, a risk ranking method is set up, and corresponding risk control methods are provided according to the risk ranking method.

Systems and methods for detecting and thwarting attacks on a computing system. The methods comprise: collecting timestamped data from different software products comprising a unified end point management product, an SBC / ADV product, an application delivery controller product, a content collaboration product, and / or a software defined WAN product; analyzing the collected timestamped data to determine if an observed user behavior matches a learned normal user behavior of an authorized user associated with a user account; determining a risk classification level associated with a credential used by a user to log into the user account, when the observed user behavior does not match the learned normal user behavior of the authorized user; and causing at least one security related action to be performed when the risk classification level is greater than a threshold level or the risk classification level is one of a top N highest risk classification levels.

Customer communication security vulnerabilities are resolved. A usage history is obtained for a user device including communications involving the user device. Pattern recognition is applied to the usage history. The user device is assigned with a risk classification from a predetermined set of possible risk classifications, based on the pattern recognition. A vulnerability on the user device is remedied when the risk classification exceeds a predetermined threshold.

A method involves receiving digital on blockchain information and digital off blockchain information. The digital data from the digital on blockchain information and the digital off blockchain information is extracted. The entity knowledge base engine contextualizes the relationships based on the digital data and the digital off blockchain information and the digital on blockchain information. The risk classification engine analyzes the digital data and transforms the digital data to an identified behavior category. The risk scoring regression engine analyzes the classified risk data and assigns a risk score to each classified risk data. The risk policy engine analyzes the classified risk data and determines if any deviations from rules or standards have or will occur. The security control system takes an action on the digital on blockchain information and digital off blockchain information based on the assigned risk score and any deviations from rules or standards.

The invention relates to big data analysis, and provides a method for determining a collector based on a classification model, and a storage medium. The method includes: after receiving a collection request of a general preference case, obtaining user identity identification information corresponding to the HP case; obtaining overdue information, collection information and account information of the user within a predefined time period according to the user identity identification information; The overdue information and collection information obtained are statistically analyzed to obtain theoverdue characteristic data of the user. According to the user overdue risk classification model trained in advance, the statistical overdue characteristic data are analyzed to obtain the overdue riskcategory of the user. According to the mapping relationship between the pre-determined collector and the overdue risk category, the collector corresponding to the generalized preference case is determined, and the generalized preference case is assigned to the determined collector for collection. The method can improve the collection business of the collectors of inclusive loan cases, which is conducive to the healthy development of the inclusive loan industry. In addition, the invention also provides an electronic device and a storage medium.

The invention discloses a catering food safety dynamic supervision method based on big data analysis.the method includes that data access is carried out, wherein the data access is used for accessingcatering industry data including government supervision department data, catering unit self-inspection data and social evaluation data; a big data cloud computing technology is applied to comprehensively evaluate catering units including data of AI video analysis non-standard behaviors, place environment and equipment operation Internet of Things sensing, electronic machine account data, personneltraining and self-checking implementation daily management in a certain period; and the food safety index risk factor statistics is used for performing risk classification and quantitative rating onthe accessed data according to the catering safety risk factor statistics and the established risk model to obtain a catering enterprise safety score.

The invention relates to a hazardous chemicals transportation risk prediction method based on big data. The method comprises the following steps: aiming at the low-probability high-risk characteristics of a hazardous chemicals transportation risk event, based on risk classification and factor correlation analysis, adding weight for each risk factor, while taking into account the spatial characteristic, transportation accident rate, exposed population, and expected loss measurement factor, integrating multiple single and quantitative prediction models through an integrated approach, to construct a hazardous chemicals transportation risk multi-dimensional factor combination forecasting model, so as to effectively improve the prediction accuracy.

The invention discloses a method and a system for rapid risk classification of rainstorm secondary geological hazards in a power network passageway. The method comprises the following steps: dividinga belt-shaped area of an important power network channel by pole and tower coordinates of a transmission line; acquiring topographic data and historical data of geological hazards in important channels of power network; according to the terrain data, dividing an important channel of the belt-shaped power network into a plurality of first-level grids; according to the topographic data of the firstgrid and the historical data of geological hazards, dividing the geological hazard grades of the important routes of the power network and the historical occurrence grades of the geological hazards ofthe important routes of the power network; according to the product of geological hazard terrain grade and geological hazard history grade, dividing the geological hazard risk grade of the importantpassageway of the power network. The invention utilizes topographic data and geological hazard historical data of an important channel of a power network, and can realize rapid division of the secondary geological hazard risk of a rainstorm in the important channel of the power network.

A method and system for administering a disease management program to improve healthcare quality, reduce healthcare costs, and optimize delivery of healthcare services. A multi-condition risk assessment is conducted for all or a substantial portion of a population of program participants, and collected multi-condition risk assessment data are combined with claims data for predictive modeling of future healthcare risk and expense. Participants are risk-stratified into one or more classifications of future healthcare cost risk, and appropriate intervention or delivery of healthcare services is made based on the risk classification.

The invention provides a method and a device for risk evaluation of electric power metering equipment based on RPN. The method analyzes the fault type and parameter, the fault influence and the faultreason of the on-site metering equipment, mainly involves the influence degree of the personnel environment, the system function, the maintenance time and the maintenance cost, then constructs the evaluation factor weight based on the AHP, finally carries on the electric power metering equipment risk evaluation. At the same time, the method of the invention can provide guidance for the maintenance of the electric power metering equipment, can solves the problem that the evaluation result is inaccurate because of the variety of electric power metering equipment, the huge quantity and the complex operation condition, and can improve the accuracy of the evaluation results and achieve the technical effect of risk classification.

A driving risk classification and prevention system for a vehicle being autonomously and manually driven, includes an orientation unit for confirming a location of the vehicle; an exterior sensing unit for sensing first information external to the vehicle; an autonomous driving risk sensing unit for sensing second information which endangers autonomous driving of the vehicle; a communications unit linked to the orientation unit for transceiving third information corresponding to the location; a risk prevention unit linked to the orientation unit, the exterior sensing unit, the autonomous driving risk sensing unit and the communications unit and used for generating a first index according to the first information, generating at least a second index according to the third information, generating a third index according to the second information, and determining whether a safety operation is performed according to at least the first index and the second index.

The invention relates to a risk matching method and device. The risk matching method comprises the steps of receiving a product subscription instruction sent by a terminal, searching a pre-stored product risk classification table according to the product subscription instruction, and extracting a product risk grade corresponding to the product identifier of a to-be-subscribed product; according tothe product subscription instruction, searching a pre-stored user risk classification table, and extracting the risk grade of a user corresponding to the identifier of the user; searching a pre-stored risk matching table according to the risk grade of the user, and extracting a product risk level corresponding to the risk grade of the user; comparing the product risk grade with the product risk level; if the product risk grade is lower than or equal to the product risk level, sending a product subscription execution instruction to the terminal; otherwise, sending the risk quota-exceeded prompt information to the terminal. According to the risk matching method, the product subscription risk can be effectively reduced. The economic loss caused by the wrong subscription of high-risk productsis avoided. Correspondingly, the invention further provides a risk matching device.

The invention discloses an antibiotic risk classification research based on cluster analysis, Based on the reported data of Adverse Drug Reaction (ADR) in China, the study compares the differences ofADR signal detection between different subsamples by PRR detection method, uses FCM method for clustering analysis, uses artificial scoring method to classify the risk of antibiotics, and uses TF-IDFAlgorithm for Feature Extraction. A method for classifying the risk of antibiotics based on clustering analysis in the adverse drug reaction report of China is provided.

The invention discloses a method for establishing an iron-overload mouse model corresponding to thalassemia Pesaro risk classification. The method comprises the following steps: injecting an iron-containing preparation to the abdominal cavity of a laboratory mouse, wherein the dosage is 1g / kg of the mouse, injecting the iron once a week, wherein the iron comprises Fe<2+> and Fe<3+>; establishing I-grade iron-overload mouse model in the Pesaro risk classification after the mouse is injected with the iron for one week; establishing II grade iron-overload mouse model in the Pesaro risk classification after the mouse is injected with the iron for 2-4 weeks; establishing III-grade iron-overload mouse model in the Pesaro risk classification after the mouse is injected with the iron for 5-8 weeks. The administration interval of the invention is in accordance with the pathological process of the hepatomegaly and hepatic fibrosis induced by the iron overload caused by repeated transfusion of the thalassemia patient, simultaneously, the mouse acute chalybeate poisoning death caused by the large-dose iron administration in a short time or the overlong mouse molding time caused by overlong iron administration time can be avoided.

The invention relates to a performance test, and provides a user risk control auditing system test method, which comprises the following steps: after receiving a user risk control auditing system testinstruction, generating and displaying a user data configuration operation interface; after receiving the at least one item of user data input by the user based on the user data configuration operation interface, analyzing the at least one item of user data according to a pre-trained decision tree model to obtain a first risk classification result of the user; obtaining a second risk classification result of the user after the user risk control auditing system carries out risk control auditing on the at least one piece of user data; and if the first risk classification result is the same as the second risk classification result, determining that the accuracy test on the user risk control auditing system is passed. The user risk control auditing system can be automatically tested, and thetest efficiency is improved. In addition, the invention further provides an electronic device and a storage medium.

The invention relates to a financial risk prediction method and device based on text pre-training and multi-task learning. The method comprises the steps of obtaining a to-be-processed text; inputting the to-be-processed text into a first neural network model to determine whether the content of the to-be-processed text includes financial risks according to the processing flow of the risk identification task; under the condition that the content of the to-be-processed text comprises the financial risk, determining the risk type of the financial risk according to the processing flow of the risk classification task by utilizing a first neural network model; and determining a risk subject matched with the risk type by using the first neural network model according to a processing flow of the risk subject identification task. According to the method and the device, the problem of poor model performance caused by lack of deep semantic mining is solved through a pre-training language model technology, and the technical problems of limited data volume and poor model performance caused by incapability of information sharing among tasks are solved by adopting multi-task processing.