| Patent application number | Description | Published |
|---|
| 20080301077 | System and Method for Medical Predictive Models Using Likelihood Gamble Pricing - A method for predicting survival rates of medical patients includes providing a set D of survival data for a plurality of medical patients, providing a regression model having an associated parameter vector β, providing an example x | 12-04-2008 |
| 20090006055 | Automated Reduction of Biomarkers - A list of biomarkers indicative of patient outcome is reduced. A computer program is applied to a set of biomarkers indicative of a patient outcome (e.g., prognosis, diagnosis, or treatment result). The computer program models the set of biomarkers with a subset of the biomarkers. The subset is identified without labeling based on the patient outcome. Instead, biomarker scores (e.g., sequence score) are used to identify the subset of biomarkers. | 01-01-2009 |
| 20090130096 | Gene Signature of Early Hypoxia to Predict Patient Survival - The present invention provides methods and compositions for predicting patient responses to cancer treatment using hypoxia gene signatures. These methods can comprise measuring in a biological sample from a patient the levels of gene expression of a group of the genes designated herein. The present invention also provides for microarrays that can detect expression from a group of genes. | 05-21-2009 |
| 20090187522 | System and Method for Privacy Preserving Predictive Models for Lung Cancer Survival Analysis - A computer-implemented method for privacy-preserving data mining to determine cancer survival rates includes providing a random matrix B agreed to by a plurality of entities, wherein each entity i possesses a data matrix A | 07-23-2009 |
| 20090234628 | PREDICTION OF COMPLETE RESPONSE GIVEN TREATMENT DATA - A system for modeling complete response prediction is provided. The system includes an input that is operable to receive treatment information representing treatment data that may be used to predict a complete response of a tumor. The complete response may include a disappearance of all or substantially all of a disease. A processor may be operable to use a model to predict complete response of the tumor as a function of the treatment data. The model represents a probability of complete response to treatment given the treatment data. A display is operable to output an image as a function of the complete response prediction. | 09-17-2009 |
| 20100057651 | Knowledge-Based Interpretable Predictive Model for Survival Analysis - Knowledge-based interpretable predictive modeling is provided. Expert knowledge is used to seed training of a model by a machine. The expert knowledge may be incorporated as diagram information, which relates known causal relationships between predictive variables. A predictive model is trained. In one embodiment, the model operates even with a missing value for one or more variables by using the relationship between variables. For application, the model outputs a prediction, such as the likelihood of survival for two years of a lung cancer patient. A graphical representation of the model is also output. The graphical representation shows the variables and relationships between variables used to determine the prediction. The graphical representation is interpretable by a physician or other to assist in understanding. | 03-04-2010 |
| 20110071967 | Automatic Labeler Assignment - A method, including receiving multi-labeler data that includes data points labeled by a plurality of labelers; building a model from the multi-labeler data, wherein the model includes an input variable that corresponds to the data points, a label variable that corresponds to true labels for the data points, and variables for the labels given by the labelers; and executing the model, in response to receiving new data points, to determine a level of expertise of the labelers for the new data points. | 03-24-2011 |
| 20110078145 | Automated Patient/Document Identification and Categorization For Medical Data - A method, including receiving a data source selection from a user or software application, the data source including medical information of a plurality of patients, receiving, from the user or software application, a data pattern that is related to a concept to be explored in the data source, querying the data source to find information that approximately matches the data pattern; and receiving the information from the data source, wherein the information includes unstructured data, assigning a classification to individual parts of the information based on the part's relationship to the data pattern, and outputting the classified information to the user or software application. | 03-31-2011 |
