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.

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 has been devised and reduced to practice. 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. Both single parameter set and multiple parameter set analyses are supported. Risk to value estimates of candidate decisions are also calculated.

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 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 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, apparatus, and computer instructions for improved control of clinical trials. In a preferred embodiment, after a clinical trial is initiated, data is regularly cleaned and processed to statistically analyze the data. The outcome includes a predictive measure of the timing and level by which the study will achieve one or more statistically significant levels, allowing mid-course modifications to the study (e.g., in population size, termination, etc.). Modification can be planned as part of the initial protocol, using thresholds or other appropriate criteria relating to the statistical outcome, making possible pre-approved protocol changes based on the statistical findings. This process has significant implications for the management of clinical studies, including ensuring the minimum possible time and number of patients are used in clinical studies to either prove (or disprove) the clinical efficacy of drugs or treatments.

Apparatus and method for the playing of a multi-stage multi-player future event outcome prediction game. The prediction game involves the prediction or forecasting or guessing of the actual outcomes of a series of pre-defined sequentially occurring future events. The objective of the players participating in the prediction game is to predict correctly, progressively, sequentially and continuously the actual outcome of the entire set of pre-defined future events included in a game unit.

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.

Diffusion weighted images and perfusion weighted images are acquired with an MRI system and used to produce low b, DWI, ADC, CBV, CBF, and MTT images of brain tissues following an ischemic event. These MRI physiological measurements are input along with a spatial location measurement to a generalized linear model that predicts the outcome of tissues surrounding a lesion.

A computer-implemented method of predicting outcomes of hypothetical events which can occur during a game of baseball, includes: accumulating and storing in a computer memory a statistical database of the cumulative effects of latency and engrams specific to an individual batter having individual batter capabilities and an individual pitcher having individual pitcher capabilities; selecting a pitch, by a user, from amongst pitches compatible with the individual pitcher capabilities; selecting a swing, by a user, from amongst swings compatible with the individual batter capabilities; computing in a computer processor a statistical performance of the individual pitcher of the selected pitch; computing in a computer processor a statistical performance of the individual batter of the selected swing; and matching in a computer processor the statistical performance of the individual pitcher with the statistical performance of the individual batter so as to compute an outcome.

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.

The invention relates to methods for predicting an outcome of cancer in a patient suffering from cancer, said patient having been previously diagnosed as node positive and treated with cytotoxic chemotherapy, said method comprising determining in a biological sample from said patient an expression level of a plurality of genes selected from the group consisting of ACTG1, CAl2, CALM2, CCND1, CHPT1, CLEC2B, CTSB, CXCL13, DCN, DHRS2, EIF4B, ERBB2, ESR1, FBXO28, GABRP, GAPDH, H2AFZ, IGFBP3, IGHG1, IGKC, KCTD3, KIAA0101, KRT17, MLPH, MMP1, NAT1, NEK2, NR2F2, OAZ1, PCNA, PDLIM5, PGR, PPIA, PRC1, RACGAP1, RPL37A, SOX4, TOP2A, UBE2C and VEGF; ABCB1, ABCG2, ADAM15, AKR1C1, AKR1C3, AKT1, BANF1, BCL2, BIRC5, BRMS1, CASP10, CCNE2, CENPJ, CHPT1, EGFR, CTTN, ERBB3, ERBB4, FBLN1, FIP1L1, FLT1, FLT4, FNTA, GATA3, GSTP1, Herstatin, IGF1R, IGHM, KDR, KIT, CKRT5, SLC39A6, MAPK3, MAPT, MKI67, MMP7, MTA1, FRAP1, MUC1, MYC, NCOA3, NFIB, OLFM1, TP53, PCNA, PI3K, PPERLD1, RAB31, RAD54B, RAF1, SCUBE2, STAU, TINF2, TMSL8, VGLL1, TRA@, TUBA1, TUBB, TUBB2A.

The invention provides an object matching method, device and equipment and a medium. The method comprises the following steps of using a server for acquiring an to-be-matched object set, wherein the to-be-matched object set comprises at least two to-be-matched objects, and each to-be-matched object comprises at least one kind of attribute information; generating M prediction combinations accordingto the to-be-matched object set, wherein each prediction combination comprises at least two queues, and each queue at least comprises one to-be-matched object; acquiring matching probability information corresponding to each prediction combination through matching prediction models, wherein the matching prediction models comprise an outcome prediction model, the outcome prediction model takes theattribute information corresponding to each object in the queues as input and takes the defeat probability as output; determining a target matching result according to the matching probability information corresponding to each prediction combination. With the adoption of the method, the divergence caused by a large strength gap between players can be effectively avoided, and a matching result ofobjects for a group battle can be optimized.

Disclosed is a medical outcome prediction tool that predicts an individual patient's medical outcomes by identifying patients having a same disease; selecting a set of characteristics unique to an individual; determining the similarities between the individual and other cases; and calculating the expected outcome for the individual that is proportional to a weighted sum of outcomes of similar cases. The similarities can be determined by calculating the number of matches between the individual and cases over the set of characteristics, and using that result to determine a similarity score.

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.

Systems, methods, and computer program products are described for generating and using a computer model that models development of a plant based on empirical data. The computer model may eliminate assumptions regarding growing conditions in order to improve outcome predictions. A method may include recommending, using the computer model, different growing conditions based on a particular plant species and observed growing conditions and / or phenotypic features of the plant. The observed growing conditions may be input by sensors and / or a grower. A method may include generating, using the computer model, a planning schedule that includes optimum growing conditions that facilitates optimal scheduling for plant being cultivated. The recommendations and / or planning schedule may facilitate comparison of phenotypic features of a model plant grown under optimal conditions and a plant being grown by a grower.

A system to prevent livelock. An outcome of an event is predicted to form an event outcome prediction. The event outcome prediction is compared with a correct value for a datum to be accessed. An instruction is appended with a real event outcome when the outcome of the event is mispredicted to form an appended instruction. A prediction override bit is set on the appended instruction. Then, the appended instruction is executed with the real event outcome.

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.

A decision support system for medical therapy planning is provided. For decision support in a medical therapy, machine learning provides a machine-learned generator for generating a prediction of outcome for therapy personalized to a patient. Deep learning may result in features more predictive of outcome than handcrafted features. More comprehensive learning may be provided by using multi-task learning, where one of the tasks (e.g., segmentation, non-image data, and / or feature extraction) is unsupervised and / or draws on a greater number of training samples than available for outcome prediction alone.

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 an improved docket search and analytics engine for determining the outcome of a case for a particular entity or party, for predicting the outcome of a case for a particular entity or party, or for predicting the time to resolution of a case for a particular entity or party. More specifically, the present invention provides a system and engine for accessing and retrieving docket and other data from a plurality of databases and applying by one or more engines a set of models to the retrieved data to make a determination or prediction as to the outcome of a case for an entity or party involved in the case.

The invention discloses a depth learning based smart instrument detection method. The method includes taking a picture of an instrument through a camera, performing preprocessing on the image throughan IPC and reading instrument numerical value through image number recognition technology; performing analysis on the read instrument numerical value data and making result prediction and judgment; sending an instrument change alarm if instrument damage is judged through analysis and taking corresponding halt treatment; sending an alarm and stopping equipment operation immediately if the instrument works normally but the numerical value surpasses a safety range of the instrument; performing smart learning on history data and previous judgment and returning to the system, improving data analysis and system pre-judgment continuously, improving the pre-judgment accuracy through uninterrupted machine self learning. The method provided by the invention has advantages of high smart level and good pre-judgment capability. Besides, the pre-judgment method is improved continuously through history data and history judgment and the smart pre-judgment capability is improved.