Abstract: A detection framework that matches anatomical structures using appearance and shape is disclosed. A training set of images are used in which object shapes or structures are annotated in the images. A second training set of images represents negative examples for such shapes and structures, i.e., images containing no such objects or structures. A classification algorithm trained on the training sets is used to detect a structure at its location. The structure is matched to a counterpart in the training set that can provide details about the structure's shape and appearance.

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: 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 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.

Abstract: A technique is provided for automatically generating performance measurement information. At least some of the obtained performance measurement information may be derived from unstructured data sources, such as free text physician notes, medical images, and waveforms. The performance measurement may be sent to a health care accreditation organization. The health care accreditation organization can use the performance measurement to evaluate a health care provider for its quality of patient care. Alternatively, performance measurement information can be provided directly to consumers.

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: A method and system correlate candidate information and provide batch classification of a number of related candidates. The batch of candidates may be identified from a single data set. There may be internal correlations and/or differences among the candidates. The candidates may be classified taking into consideration the internal correlations and/or differences. The locations and descriptive features of a batch of candidates may be determined. In turn, the locations and/or descriptive features determined may used to enhance the accuracy of the classification of some or all of the candidates within the batch. In one embodiment, the single data set analyzed is associated with an internal image of patient and the distance between candidates is accounted for. Two different algorithms may each simultaneously classify all of the samples within a batch, one being based upon probabilistic analysis and the other upon a mathematical programming approach. Alternate algorithms may be used.

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: A method for segmenting an anatomical structure of interest within an image is disclosed. The anatomical structure of interest is compared to a database of images of like anatomical structures. Those database images of like anatomical structures that are similar to the anatomical structure of interest are identified. The identified database images are used to detect the anatomical structure of interest in the image. The identified database images are also used to determine the shape of the anatomical structure of interest. The anatomical structure of interest is segmented from the image.

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 medical concept is learned about or inferred from a medical transcript. A probabilistic model is trained from medical transcripts. For example, the problem is treated as a graphical model. Discrimitive or generative learning is used to train the probabilistic model. A mutual information criterion can be employed to identify a discrete set of words or phrases to be used in the probabilistic model. The model is based on the types of medical transcripts, focusing on this source of data to output the most probable state of a patient in the medical field or domain. The learned model may be used to infer a state of a medical concept for a patient.

Abstract: Medical ontology information is used for mining and/or probabilistic modeling. A domain knowledge base may be automatically or semi-automatically created by a processor from a medical ontology. The domain knowledge base, such as a list of disease associated terms, is used to mine for corresponding information from a medical record. The relationship of different terms with respect to a disease may be used to train a probabilistic model. Probabilities of a disease or chance of indicating the disease are determined based on the terms from a medical ontology. This probabilistic reasoning is learned with a machine from ontology information and a training data set.

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: A method for computer aided detection of anatomical abnormalities in medical images includes providing a plurality of abnormality candidates and features of said abnormality candidates, and classifying said abnormality candidates as true positives or false positives using a hierarchical cascade of linear classifiers of the form sign(wTx+b), wherein x is a feature vector, w is a weighting vector and b is a model parameter, wherein different weights are used to penalize false negatives and false positives, and wherein more complex features are used for each successive stage of said cascade of classifiers.

Abstract: Quality improvement factors in patient care are ranked. Hospital performance is measured, such as a CMS measure. The variables and/or values relative contribution to quality of care is determined using medical records of the hospital. The variables and/or values are ranked according influence of the quality of care result. The ranking is performed by a given medical institution at a desired time rather than based on a broad study. The medical institution may regularly determine variables (e.g., admitting doctor) and/or values (e.g., doctor X) that are relevant to a decreased quality of care. Quality may be regularly improved using a software product.

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: A detection framework that matches anatomical structures using appearance and shape is disclosed. A training set of images are used in which object shapes or structures are annotated in the images. A second training set of images represents negative examples for such shapes and structures, i.e., images containing no such objects or structures. A classification algorithm trained on the training sets is used to detect a structure at its location. The structure is matched to a counterpart in the training set that can provide details about the structure's shape and appearance.

Abstract: Medical ontology information is used for mining and/or probabilistic modeling. A domain knowledge base may be automatically or semi-automatically created by a processor from a medical ontology. The domain knowledge base, such as a list of disease associated terms, is used to mine for corresponding information from a medical record. The relationship of different terms with respect to a disease may be used to train a probabilistic model. Probabilities of a disease or chance of indicating the disease are determined based on the terms from a medical ontology. This probabilistic reasoning is learned with a machine from ontology information and a training data set.