Abstract: A method for multiple-label data analysis includes: obtaining labeled data points from more than one labeler; building a classifier that maximizes a measure relating the data points, labels on the data points and a predicted output label; and assigning an output label to an input data point by using the classifier.

Abstract: Modeling of prognosis of survivability, side-effect, or both is provided. For example, RILI is predicted using bullae information. The amount, volume or ratio of Bullae, even alone, may indicate the likelihood of complication, such as the likelihood of significant (e.g., stage 3) pneumonitis. As another example, RILI is predicted using uptake values of an imaging agent. Standardized uptake from a functional image (e.g., FDG uptake from a positron emission image), alone or in combination with other features, may indicate the likelihood of side-effect. In another example, survivability, such as two-year survivability, is predicted using blood biomarkers. The characteristics of a patient's blood may be measured and, alone or in combination with other features, may indicate the likelihood of survival. The modeling may be for survivability, side-effect, or both and may use one or more of the blood biomarker, uptake value, and bullae features.

Abstract: 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.

Abstract: The present invention proposes a method and a system for dynamic cache partitioning for application tasks in a multiprocessor. An approach for dynamically resizing cache partitions based on the execution phase of the application tasks is provided. The execution phases of the application tasks are identified and updated in a tabular form. Cache partitions are resized during a particular instance of the execution of application tasks such that the necessary and sufficient amount of cache space is allocated to the application tasks at any given point of time. The cache partition size is determined according to the working set requirement of the tasks during its execution, which is monitored dynamically or statically. Cache partitions are resized according to the execution phase of the task dynamically such that unnecessary reservation of the entire cache is avoided and hence an effective utilization of the cache is achieved.

Abstract: Automated treatment planning is provided with individual specific consideration. One or more prognosis models indicate survivability as a function of patient specific information for a given dose. By determining survivability for a plurality of doses, the biological model represented by survivability as a function of dose is determined from the specific patient. Similarly, the chances of complications or side effects are determined. The chance of survivability and chance of complication are used as or instead of the tumor control probability and normal tissue complications probability, respectively. The desired tumor dosage and tolerance dosage are selected as a function of the patient specific dose distributions. The selected dosages are input to an inverse treatment planning system for establishing radiation treatment parameters.

Abstract: CAD (computer-aided diagnosis) systems and applications for cardiac imaging are provided, which implement methods to automatically extract and analyze features from a collection of patient information (including image data and/or non-image data) of a subject patient, to provide decision support for various aspects of physician workflow including, for example, automated assessment of regional myocardial function through wall motion analysis, automated diagnosis of heart diseases and conditions such as cardiomyopathy, coronary artery disease and other heart-related medical conditions, and other automated decision support functions. The CAD systems implement machine-learning techniques that use a set of training data obtained (learned) from a database of labeled patient cases in one or more relevant clinical domains and/or expert interpretations of such data to enable the CAD systems to “learn” to analyze patient data and make proper diagnostic assessments and decisions for assisting physician workflow.

Abstract: The present invention provides methods and compositions for predicting patient responses to cancer treatment using a proliferation gene signature. 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.

Abstract: A system and method for diagnosis and treatment decisions based on information maximization is disclosed. Utilizing patient information as well as clinical records from other patients can reduce the uncertainty in both diagnosis and treatment options. The information maximization may consider additional data such as risk, cost, and comfort in making a proper medical decision.

Abstract: Embodiments of the present invention relate to systems, methods, and computer storage media for geographically organizing the storage of data and hosted services in a distributed computing environment. An allocation constraint is utilized to identify and select a geo-region in which an affinity group that is comprised of a hosted service and/or data is to be physically stored. Additionally, a geo-domain within the geo-region is identified for hosting the hosted service and/or data of the affinity group. A stamp, such as a storage stamp or a computation stamp, is identified on the geo-domain for storing the hosted service and/or the data. The hosted service is stored in an identified computation stamp and the data is stored in an identified storage stamp. In an additional exemplary embodiment, the identification of a geo-domain involves analyzing resource information related to potential geo-domains within a geo-region.

Abstract: Systems and methods for utilizing affinity groups to allocate data items and computing resources are disclosed. Upon receipt of a user preference indicating an affinity group, a token associated with that affinity group may be stored in a database. The affinity group may be associated with a geographic region or a number of data centers. Data items and computing resources may be associated with the affinity group. These data items and computing resources may be allocated to a geographic region or data center based on their association with the affinity group. These data items and computing resources may also be reallocated based on efficiency analyses or user preferences. In this way, data items and computing resources may be efficiently allocated with lower user effort.

Abstract: Automated treatment planning is provided with individual specific consideration. One or more prognosis models indicate survivability as a function of patient specific information for a given dose. By determining survivability for a plurality of doses, the biological model represented by survivability as a function of dose is determined from the specific patient. Similarly, the chances of complications or side effects are determined. The chance of survivability and chance of complication are used as or instead of the tumor control probability and normal tissue complications probability, respectively. The desired tumor dosage and tolerance dosage are selected as a function of the patient specific dose distributions. The selected dosages are input to an inverse treatment planning system for establishing radiation treatment parameters.

Abstract: A predictor of medical treatment outcome is developed and applied. A prognosis model is developed from literature. The model is determined by reverse engineering the literature reported quantities. A relationship of a given variable to a treatment outcome is derived from the literature. A processor may then use individual patient values for one or more variables to predict outcome. The accuracy may be increased by including a data driven model in combination with the literature driven model.

Abstract: A system and method for screening for coronary heart disease is provided. The method includes the steps of retrieving a test for assessing risk of coronary heart disease, the test including a plurality of data fields relating to coronary risk factors; accessing a database to populate the data fields with information of an individual patient; and calculating a risk assessment of the individual patient developing coronary heart disease. A system includes a first database including a plurality of structured computerized patient records; a second database including a knowledge base relating to coronary heart disease, the second database including at least one test for determining coronary heart disease risk; and a processor for retrieving the at least one test from the second database, populating the at least one test with patient information retrieved from the first database and calculating a risk assessment for at least one patient.

Abstract: The present invention is directed to a method for automatic detection and segmentation of a target anatomical structure in received three dimensional (3D) volumetric medical images using a database of a set of volumetric images with expertly delineated anatomical structures. A 3D anatomical structure detection and segmentation module is trained offline by learning anatomical structure appearance using the set of expertly delineated anatomical structures. A received volumetric image for the anatomical structure of interest is searched online using the offline learned 3D anatomical structure detection and segmentation module.

Abstract: A system and method for screening for coronary heart disease is provided. The method includes the steps of retrieving a test for assessing risk of coronary heart disease, the test including a plurality of data fields relating to coronary risk factors; accessing a database to populate the data fields with information of an individual patient; and calculating a risk assessment of the individual patient developing coronary heart disease. A system includes a first database including a plurality of structured computerized patient records; a second database including a knowledge base relating to coronary heart disease, the second database including at least one test for determining coronary heart disease risk; and a processor for retrieving the at least one test from the second database, populating the at least one test with patient information retrieved from the first database and calculating a risk assessment for at least one patient.

Abstract: In a multinode computer network, a directory database (DDB) in each of the nodes, contents of the DDB being replicated throughout the network through use of one of its nodes having been appointed as master node. The master node updates each DDB in each node in its network or domain configuration when the configuration changes. A global administrator has authority to replace or select a master node and to configure a domain, and performs these and other functions by way of computer terminal screen dialogs offered by a graphical user interface (GUI) associated with the computer network. In the case of multiple master nodes attempting to be master for the same nodes in the same network at the same time, this conflict is resolved in one embodiment of the present invention by allowing the most recently selected purported master node to be the actual master node. After resolution of this conflict the result is communicated by the prevailing master node to all nodes in the network.

Abstract: CAD (computer-aided diagnosis) systems and applications for breast imaging are provided, which implement methods to automatically extract and analyze features from a collection of patient information (including image data and/or non-image data) of a subject patient, to provide decision support for various aspects of physician workflow including, for example, automated diagnosis of breast cancer other automated decision support functions that enable decision support for, e.g., screening and staging for breast cancer. The CAD systems implement machine-learning techniques that use a set of training data obtained (learned) from a database of labeled patient cases in one or more relevant clinical domains and/or expert interpretations of such data to enable the CAD systems to “learn” to analyze patient data and make proper diagnostic assessments and decisions for assisting physician workflow.

Abstract: A system and method for providing the capability of peeling thin polymer films from a substrate is provided. Generally, the method contains the steps of: providing a substrate having a top surface and a bottom surface; placing a peel initiator on the top surface; applying a polymer on the top surface of the substrate and a top surface of the peel initiator; curing the polymer, resulting in a thin-polymer film; and peeling the peel initiator from the substrate, thereby removing the thin-polymer film from the top surface of the substrate.

Abstract: Systems and methods are provided for automated assessment of regional myocardial function using wall motion analysis methods that analyze various features/parameters of patient information (image data and non-image data) obtained from medical records of a patient. For example, a method for providing automatic diagnostic support for cardiac imaging generally comprises obtaining image data of a heart of a patient, obtaining features from the image data of the heart, which are related to motion of the myocardium of the heart, and automatically assessing regional myocardial function of one or more regions of a myocardial wall using the obtained features.

Abstract: A method and an apparatus display marks in an image data set, wherein an image data set comprising marks is provided and wherein during a review phase not all marks within the image data set are displayed at the same time. A list of the marks can be generated by sorting the marks depending on a predetermined sorting criterion and wherein the marks are displayed temporally one after another within the image data set in accordance with the generated list. The image data set is for example a medical image data set, wherein the marks are computer-aided detection (CAD) marks and wherein the sorting criterion is the probability of marking illness, in particular the suspiciousness.