61 results about "Outcome prediction" patented technology

The system described herein enables clinicians and researchers to use aggregated genetic and phenotypic data from clinical trials and medical records to make the safest, most effective treatment decisions for each patient. This involves (i) the creation of a standardized ontology for genetic, phenotypic, clinical, pharmacokinetic, pharmacodynamic and other data sets, (ii) the creation of a translation engine to integrate heterogeneous data sets into a database using the standardized ontology, and (iii) the development of statistical methods to perform data validation and outcome prediction with the integrated data. The system is designed to interface with patient electronic medical records (EMRs) in hospitals and laboratories to extract a particular patient's relevant data. The system may also be used in the context of generating phenotypic predictions and enhanced medical laboratory reports for treating clinicians. The system may also be used in the context of leveraging the huge amount of data created in medical and pharmaceutical clinical trials. The ontology and validation rules are designed to be flexible so as to accommodate a disparate set of clients. The system is also designed to be flexible so that it can change to accommodate scientific progress and remain optimally configured.

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 system and method for event outcome prediction may include a processor configured to receive via a user interface a user-selection of a subset of a plurality of listed statistical categories, and rank participants of the event based selectively on analysis of the statistical information concerning the selected subset of categories. The system may output the ranked list as a predicted outcome, and may further output a user interface via which to place a bet on the predicted outcome.

Systems and methods for multi-modal, multi-resolution deep learning neural networks for segmentation, outcomes prediction and longitudinal response monitoring to immunotherapy and radiotherapy are detailed herein. A structure-specific Generational Adversarial Network (SSGAN) is used to synthesize realistic and structure-preserving images not produced using state-of-the art GANs and simultaneously incorporate constraints to produce synthetic images. A deeply supervised, Multi-modality, Multi-Resolution Residual Networks (DeepMMRRN) for tumor and organs-at-risk (OAR) segmentation may be used for tumor and OAR segmentation. The DeepMMRRN may combine multiple modalities for tumor and OAR segmentation. Accurate segmentation is may be realized by maximizing network capacity by simultaneously using features at multiple scales and resolutions and feature selection through deep supervision. DeepMMRRN Radiomics may be used for predicting and longitudinal monitoring response to immunotherapy. Auto-segmentations may be combined with radiomics analysis for predicting response prior to treatment initiation. Quantification of entire tumor burden may be used for automatic response assessment.

A system for fully integrated collection of business impacting data, analysis of that data and generation of both analysis-driven business decisions and analysis driven simulations of alternate candidate business actions. This business operating system may be used predict the outcome of enacting candidate business decisions based upon past and current business data retrieved from both within the corporation and from a plurality of external sources pre-programmed into the system. Simulations using this data and predefined parameters to create models of actors are then run. Risk to value estimates of candidate decisions are also calculated.

Contemplated systems and methods allow for prediction of chemotherapy outcome for patients diagnosed with high-grade bladder cancer. In particularly preferred aspects, the prediction is performed using a model based on machine learning wherein the model has a minimum predetermined accuracy gain and wherein a thusly identified model provides the identity and weight factors for omics data used in the outcome prediction.

Methods identifying prostate cancer, methods for prognosing and diagnosing prostate cancer, methods for identifying a compound that modulates prostate cancer development, methods for determining the efficacy of a prostate cancer therapy, and oligonucleotide microarrays containing probes for genes involved in prostate cancer development are described.

Outcome predictions based on historical data relating to events in a “game” (which can be a sports context, an e-sports contest, a business situation, a medical situation, or other scenarios) can be generated using a watcher process that receives event data and triggers conversion of a “different” last event received into arguments for one or more analysis protocols, which are executed against a data model created by an analysis engine. Methods, systems, articles of manufacture, and the like are described.

The present invention relates to a method and apparatus to objectively measure stress and fatigue using measurement tools, record stress and fatigue related data in a standardized database, create automated prompts and alerts based upon pre-defined stress and fatigue thresholds (which are derived based upon individual end-user and task performance), provide a number of interventions (which can be preferentially selected by the individual end-user), create data-driven best practice guidelines though meta-analysis of the database, and provide an objective tool for comparative technology assessment.

A Physical Fault Analysis (PFA) outcome prediction tool utilizes previously-generated evaluation data and associated PFA outcome data to generate a Bayesian Generalized Linear Model (BGLM), and then utilizes the BGLM to generate a PFA outcome prediction for newly-submitted evaluation data that operably characterizes measured operating characteristics of an IC chip that is being developed. The BGLM generation methodology by utilizing a Generalized Linear Model (GLM) in a Bayesian framework to form a hierarchical model representing the evaluation data and associated PFA outcome data as a linear combination. The PFA outcome prediction includes a credible interval of a posterior distribution that effectively represents a cross-sectional portion of the BGLM corresponding to the newly-submitted evaluation data. The previously-generated evaluation data and associated PFA outcome data are stored in a training data library, which is updated to include newly-submitted evaluation data and associated PFA outcome after each PFA is performed.

A method for customer-based outcome prediction that includes receiving recordings of interactions with customers in a customer group, analyzing the recordings of the interactions to generate interaction data, and building a predictive model using the interaction data, the predictive model identifying a variable relevant to predicting a likelihood of an identified outcome occurring in association with future interactions with customers in the customer group. The method also includes receiving a recording of a current interaction with a first customer, the first customer being in the customer group and analyzing the recording of the current interaction with the first customer to generate current interaction data. Further, the method includes adding the current interaction data to a first customer profile associated with the first customer and applying the predictive model to the first customer profile to predict the likelihood of the identified outcome occurring in association with the current interaction.

The present invention provides a method for providing real-time support in interpreting medical record data and monitoring parameters received from a patient in order to assess the possible outcomes and treatment history for the patient based upon prior patients having similar monitoring parameters and medical record data. The system includes a monitoring device that obtains monitoring parameters from the patient and communicates with a historic reference database that includes numerous historic, stored patient records. Upon receiving the monitoring parameters and medical record data for the patient, the historic reference database identifies one or more historic patient records that closely correspond to the patient being treated. Based upon the identified historic patient records, the physician is presented with a likely outcome and proposed course of treatment.