129 results about "Cancer risk assessment" patented technology

A system for analyzing risk using machine learning models may be trained using a data set to generate a risk assessment model that is optimized for metrics commonly used in for financial risk evaluation. The metrics may include Gini and CaptureRate, for example. The system may receive a request for a financial service, and generate a risk assessment by applying the risk assessment model to factors associated with the request. The system may also decide on the request in response to the risk assessment.

A money transfer system includes a risk assessment system and a risk modeling system. The risk assessment system uses rules engines and Z scores to assess, on a real time basis, risk factors associated with money transfers. The risk modeling system develops risk scores based on historical transaction data in order to create a risk model. The risk model may be analyzed over time to refine the rules engines and take other actions to understand and reduce the risk of improper activity in connection with money transfers.

A cloud-based risk assessment service collects industrial data from multiple relevant and connected sources for storage and analysis on a cloud platform. The service analyzes gathered data from internal and external sources and customers across different industries to identify operational trends as a function of industry type, application type, equipment in use, device configurations, and other such variables. Based on the analysis, the risk assessment service identifies risk factors inherent in a customer's particular industrial enterprise. The cloud-based system generates a risk profile for the customer that identifies the determined risks and recommends risk aversion strategies based on the customer's specific profile, compared to industry standards, product information, internal business expectations, external regulatory bodies, and/or past performance. Risk profiles are tailored for both plant-level users and business-level users to provide intelligent strategies to improve performance and prevent avoidable losses.

The nonlinear complexity of EEG signals is believed to reflect the scale-free architecture of the neural networks in the brain. Analysis of the complexity and synchronization of EEG signals as described herein provides a quantitative measure for routine monitoring of functional brain development in infants and young children and provide a useful biomarker for detecting functional abnormalities in the brain before the cognitive, behavioral or social manifestations of these brain developments can be observed and measured by standard tests. One or more machine learning algorithms are used to discover relevant patterns in the complexity and synchronization values determined from the EEG data to facilitate risk assessment and/or diagnosis of developmental disorders in infants and young children by predicting cognitive, behavioral and social outcomes of the measured functional brain activity patterns.

An apparatus, system and process for tracking and analyzing target person movements, captured while the target person is performing ordinary tasks outside of a medical context, for medical diagnosis and treatment review are described. The method may include constructing a model of a target person from three-dimensional (3D) image data of the target person performing an activity over a period of time. The method may also include tracking movement of the model of the target person in the 3D image data over the period of time, and detecting one or more motion features in the movement of the model of the target person that are relevant to diagnosis, treatment, care, or a combination thereof, of a potential chronic neurodegenerative or musculoskeletal medical condition. The method may also include computing a risk score associated with likelihood of the target person having the medical condition based on the detected motion features.

According to a preferred aspect of the instant invention, there is provided herein a system and method for extending zero-offset or stacked wave-equation illumination analysis into the angle-gather domain, where it becomes an appropriate tool for assessing the effects of complex overburden on AVA response. A preferred method for doing this involves first creating an angle gather that has a perfect AVA response (i.e. a constant amplitude as a function of angle). This gather is then preferably used as a reflectivity map that is fed into a demigration process which creates modeled data that by construction carries with it a completely flat reflectivity signature. Remigration of such a data set then results in a gather on which any amplitude variation is more likely to be a measure of illumination effects alone. The resulting AVA signature on the gather can then be used to assess the validity of the AVA response on modeled or actual data, resulting in a useful AVA risk analysis.

The invention relates to a tropical cyclone full path simulation method facing disaster risk assessment. The tropical cyclone full path simulation method comprises concrete steps: a first step of establishing a start point model, and taking samples to simulate yearly occurrence frequency of tropical cyclones and generate start point information; a second step of establishing a marching model, and simulating the moving speed and orientation of the tropical cyclones to form a tropical cyclone path; a third step of establishing strength models including an ocean surface strength development model and a land strength attenuation model, and selecting the corresponding strength model in dependence on the latitude and longitude of the next point in the tropical cyclone path; a fourth step of examining the path simulation result, and carrying out statistical result examination and statistical rule analysis on the randomly-generated tropical cyclone path and the strength large sample; and a fifth step of establishing a typhoon wind field engineering model and a boundary layer model, calibrating wind field model key parameters, calculating the typhoon wind speed on the simulated point, and carrying out typhoon risk disaster assessment. The tropical cyclone full path simulation method facing disaster risk assessment can achieve refined evaluation, and is accurate and reliable in result, high in calculating efficiency and wide in application scope.

Methods and systems of risk assessment for network access control through data analytics. An embodiment of the invention employs well-known machine-learning clustering methods to learn normal entity behavior by looking for patterns in the events that stream in continuously. In an embodiment of the invention, normal entity behaviors are represented as clusters of event vectors. An embodiment of the invention evaluates the risk level for a new event of an entity by comparing the event with the entity's profile represented as clusters of event vectors. In an embodiment of the invention, the risk level is associated with a confidence level. Confidence level indicates how well the system knows about the entity. Embodiments of the invention do not need human administration in the process of building entity profile and assessing risk level of events associated with an entity.

The present invention discloses a disease risk assessment and personalized health report generation system and method. The method comprises: inputting longitudinal health examination big data; arranging the data; defining the threshold range of various indicators of the disease according to the name of the disease, and using the sas software to establish a corresponding disease queue according to the threshold range of various indicators of the disease; and after establishing the queue, using the cox single factor regression analysis method to carry out variable selection on each variable; carrying out multi-factor regression analysis on the variables incorporated into the cox regression model; establishing a cox proportional risk model; verifying the inside of the cox proportional risk mode, and verifying the outside of the cox proportional risk model; obtaining a disease prediction model; generating a disease risk assessment hyperbola; inputting new individual physical examination indicators, generating disease risk assessment results according to the disease prediction model and the disease risk assessment hyperbola, and outputting a disease risk assessment and personalized health report according to the preset indicators.

The present invention is directed to the identification of genostatic factors, methods of determining the association of a plurality of genes with polygenic disorders, and method of assessing the sensitivity and specificity of the risk of polygenic disorders. In particular, the present invention discovers that the association between a polygenic disorder phenotype and polygenes may be masked. Incorporating genostatic factor, such as maternal age, birth disorder, the androgen receptor gene, gender, and age, into the statistical analysis of the association between phenotypes and genotypes reveals statistically significant relationship between the two. Accordingly, the present invention provides novel methods in determining the association of a plurality of genes with a polygenic disease and the likelihood of having the polygenic disease.

The invention discloses a mutant gene group for mammary cancer risk assessment and a detection kit thereof. The mutant gene group is a mutant gene set comprising the following seven genes: BRCA1 gene, BRCA2 gene, TP53 gene, MLH1 gene, MLH3 gene, MSH3 gene and CDH1 gene. The mutant gene group of the seven genes totally carries 25 mutant sites disclosed as Table 3. The invention also discloses a detection kit of the mutant gene group for mammary cancer risk assessment, which comprises probes or primers for detecting the mutant genes in the mutant gene group. The detection kit disclosed by the invention has the advantages of high detection efficiency, high detection speed, high detectable rate and the like.

A method, computer system, and computer program product for on-demand risk assessment in on-line transactions comprises: computing, by a machine intelligence application, a risk score for the individual; providing the risk score to a cache; and responsive to receiving new data regarding the individual, calculating a new risk score for the individual and replacing the risk score in the cache with the new score.

Systems and methods for use in patient fall risk assessment are provided. In the fall risk assessment system, a Protocol including one or more motion-based tests is performed by a patient. The patient's performance is recorded using one or more sensors and the patient's data is compared to normative data to assess risk within the multifactorial problem of fall risk assessment and management.

The invention relates to the field of network information security, and aims to provide a user risk assessment and analysis method based on a multi-dimensional behavior model. The user risk assessmentand analysis method based on the multi-dimensional behavior model can evaluate the user risk of an application with a log. Users are acquired by a product log; behavior features of the users are searched; behavior data form sample data; a cluster of normal behaviors is obtained by birch; lastly, normal behavior probability distribution of the user is obtained through a mixed Gauss model; and thenBayesian linear regression is adopted to obtain a linear model capable of accurately generalizing normal behaviors of the users. Through adoption of the method, risk assessment can be performed on online user states accurately, efficiently and intelligently.

The present invention relates to a method for prognosis of an outcome or assessing the risk of a patient having suffered a stroke or a transient ischemic attack, comprising the determination of the level of at least one marker peptide in said sample said marker peptide selected from the group comprising ANP, AVP, ADM, ET-1, troponin, CRP, calcitonin and hGH or fragments thereof or its precursor or fragments thereof and attributing the level of said at least one marker peptides its precursor or fragments thereof with the prognosis of an outcome or assessing the risk for said patient.

The present invention relates to the determination of the level of marker peptides in a sample derived from a bodily fluid of a subject presenting to the emergency department with non-specific complaints.

The present invention relates to methods and systems for assessing the overall risk of a human female subject for developing a breast cancer phenotype. In particular, the present invention relates to combining clinical risk assessment and genetic risk assessment to improve risk analysis.

Embodiments of the invention relate to identification of risks in a brand commodity supply chain. Risks are centrally assessed and a single holistic image of the risk assessment is provided. Specifically, risks are assessed vertically and horizontally across the supply chain, brand, and commodity. Risk identification techniques may be applied to the identified risks to reduce supply chain disruption and to enable continued manufacturing.

The invention relates to a multivariate analysis driving risk assessment system based on people, vehicles, roads and environment. Risk assessment is carried out by using the technical scheme; the driver, the vehicles, the routes, the weather and other parties face data with influence on in-transit safety are collected; the collected information is calculated through the mathematical model to obtain in-transit risk indexes of all aspects; after the unquantized risk factors are quantized, a driver can know the driving state more visually in the driving process, when the quantized risk influencefactors exceed the safety value, the early warning module gives an alarm to achieve the reminding effect, and the in-transit driving risk can be further reduced.

The invention provides a food safety risk prediction method based on the hidden Markov model. The method comprises the steps: analyzing every step of a food supply chain, and finding out a critical control point of each step; analyzing the critical control point of each step and using the critical control point as a quantitative index of the HMM (hidden Markov model), and establishing a HMM; initializing parameters of the HMM, and training the parameters of the HMM according to a visible state sequence of the HMM and the real state of a system; and performing the risk assessment and value-at-risk calculation on the risk grade of the food supply chain. According to the invention, the HMM is adopted to assess the food quality and safety, the dynamics of every step is considered, the risk state can be responded in real time, a quantitative and qualitative-combined assessment method is adopted, the degree of the supply chain risk can be more precisely described than before, and the method is helpful for decision maker to timely adopt corresponding measures. The risk assessment model can assess the dairy product and has strong expansibility so as to apply to other fields of food safety.

The present invention discloses certain genetic variants as susceptibility variants for lung cancer. The invention relates to methods of risk assessment using such variants. The invention further relates to kits for use in risk assessment of lung cancer.