Abstract: A method for de-identification of visual media data, including: merging a sequence of images from a set of visual media data into an averaged image; bounding portions of the averaged image that are determined to be relatively fixed, wherein each bounded portion is identified by a corresponding position in the averaged image; generating a template comprising the bounded portions and the corresponding position for each bounded portion in the averaged image; and de-identifying the sequence of images by obfuscating content in the bounded portions.

Abstract: Embodiments relate to finding similar coronary angiograms in a database of coronary angiograms. An aspect includes receiving angiography data for a coronary artery, processing the angiography data to identify one or more semantic features of the coronary artery, and identifying one or more nearest coronary angiograms for each of the one or more semantic features from the database of coronary angiograms. The method also includes receiving a disease attribute associated with each of the one or more nearest coronary angiograms.

Abstract: Embodiments relate to segmenting blood vessels in angiogram images. An aspect includes a method that includes receiving and preprocessing at least one angiogram frame and preprocessing. In one embodiment, at least one angiogram frame is received and preprocessed. Bottom-up filtering of the preprocessed angiogram frame and top-down segmentation of the preprocessed angiogram frame are performed based on the results of the bottom-up filtering. The bottom-up filtering and the top-down segmentation are iteratively repeated until the difference between results of the top-down segmentation from consecutive iterations is equal to or below a threshold value. Based on determining that a difference between results of the top-down segmentation from consecutive iterations is below or equal to the threshold value, the results of the top-down segmentation are outputted.

Abstract: Embodiments relate to segmenting blood vessels in angiogram images. An aspect includes a method that includes receiving and preprocessing at least one angiogram frame and preprocessing. In one embodiment, at least one angiogram frame is received and preprocessed. Bottom-up filtering of the preprocessed angiogram frame and top-down segmentation of the preprocessed angiogram frame are performed based on the results of the bottom-up filtering. The bottom-up filtering and the top-down segmentation are iteratively repeated until the difference between results of the top-down segmentation from consecutive iterations is equal to or below a threshold value. Based on determining that a difference between results of the top-down segmentation from consecutive iterations is below or equal to the threshold value, the results of the top-down segmentation are outputted.

Abstract: Machine learning solutions compensate for data missing from input (training) data and thereby arrive at a predictive model that is based upon, and consistent with, the training data. The predictive model can be generated within a learning algorithm framework by transforming the training data to generate modality or similarity kernels. Similarity values can be generated for these missing similarity values.

Abstract: Use of medical workflows where a first medical workflow is obtained from a plurality of medical acts performed in sequence that related to care of a patient. A set of condition-indication rules is applied to the first medical workflow to determine first condition information. The first condition information relates to a likelihood that a first medical condition exists in the patient.

Abstract: Embodiments relate to associating coronary angiography image annotations with SYNTAX score for assessment of coronary artery disease. Aspects include receiving and processing a plurality of angiogram videos from a coronary angiography study into a plurality of frames, selecting and displaying a key frame from the plurality of frames for each angiogram video in a browsing interface, and receiving a selection of one of the key frame from a user. Aspects further include displaying the angiogram video associated with the selected key frame in a video viewer interface, receiving a lesion annotation from the user for a frame of the angiogram video, and displaying a SYNTAX score questionnaire in the video viewer interface. Aspects further include annotating the frame of the angiogram video with the answers to the SYNTAX score questionnaire from the user and saving the answers to the SYNTAX score questionnaire with the annotated frame in a database.

Abstract: Embodiments relate to finding similar coronary angiograms in a database of coronary angiograms. An aspect includes receiving angiography data for a coronary artery, processing the angiography data to identify one or more semantic features of the coronary artery, and identifying one or more nearest coronary angiograms for each of the one or more semantic features from the database of coronary angiograms. The method also includes receiving a disease attribute associated with each of the one or more nearest coronary angiograms.

Abstract: Embodiments relate to segmenting blood vessels in angiogram images. An aspect includes a method that includes receiving and preprocessing at least one angiogram frame and preprocessing. In one embodiment, at least one angiogram frame is received and preprocessed. Bottom-up filtering of the preprocessed angiogram frame and top-down segmentation of the preprocessed angiogram frame are performed based on the results of the bottom-up filtering. The bottom-up filtering and the top-down segmentation are iteratively repeated until the difference between results of the top-down segmentation from consecutive iterations is equal to or below a threshold value. Based on determining that a difference between results of the top-down segmentation from consecutive iterations is below or equal to the threshold value, the results of the top-down segmentation are outputted.

Abstract: Embodiments relate to segmenting blood vessels in angiogram images. An aspect includes a method that includes receiving and preprocessing at least one angiogram frame and preprocessing. In one embodiment, at least one angiogram frame is received and preprocessed. Bottom-up filtering of the preprocessed angiogram frame and top-down segmentation of the preprocessed angiogram frame are performed based on the results of the bottom-up filtering. The bottom-up filtering and the top-down segmentation are iteratively repeated until the difference between results of the top-down segmentation from consecutive iterations is equal to or below a threshold value. Based on determining that a difference between results of the top-down segmentation from consecutive iterations is below or equal to the threshold value, the results of the top-down segmentation are outputted.

Abstract: A phrase matching system is described. The system includes a training engine and a matching engine. The training engine is configured to: learn terms and term variants from a training corpus, wherein the terms and the term variants correspond to a specialized dictionary related to the training corpus; and generate a list of negative indicators found in the training corpus. The matching engine is configured to: perform a partial match of the terms and the term variants in a set of electronic documents to create initial match results; and perform a negation test using the negative indicators and a positive terms test using the terms and the term variants on the initial match results to remove matches from the initial match results that fail either the negation test or the positive terms test, resulting in final match results.

Abstract: According to one embodiment of the present invention, a method for echocardiogram view classification is provided. According to one embodiment of the present invention, a method comprises: obtaining a plurality of video images of a subject; aligning the plurality images; using the aligned images to generate a motion magnitude image; filtering the motion magnitude image using an edge map on image intensity; detecting features on the motion magnitude image, retaining only those features which lie in the neighborhood of intensity edges; encoding the remaining features by generating, x, y image coordinates, a motion magnitude histogram in a window around the feature point, and a histogram of intensity values near the feature point; and using the encoded features to classify the video images of the subject into a predetermined classification.

Abstract: Continuous wave Doppler images in a data base comprising cardiac echo studies are processed to separate Doppler frames. The frames are pre-processed to extract envelope curves and their corner shape features. Shape patterns in Doppler images from echo studies of patients with known cardiac (valvular) diseases are employed to infer the similarity in valvular disease labels for purposes of automated clinical decision support. Specifically, similarity in appearance of Doppler images from the same disease class is modeled as a constrained non-rigid translation transform of velocity envelopes embedded in these images. Shape similarity between two Doppler images is then judged by recovering the alignment transform using a variant of dynamic shape warping. Since different diseases appear as characteristic shape patterns in Doppler images, measuring the similarity in the shape pattern conveyed within the velocity region of two Doppler images can infer the similarity in their diagnosis labels.

Abstract: A method for extracting information from electronic documents, including: learning terms and term variants from a training corpus, wherein the terms and the term variants correspond to a specialized dictionary related to the training corpus; generating a list of negative indicators found in the training corpus; performing a partial match of the terms and the term variants in a set of electronic documents to create initial match results; and performing a negation test using the negative indicators and a positive terms test using the terms and the term variants on the initial match results to remove matches from the initial match results that fail either the negation test or the positive terms test, resulting in final match results.

Abstract: A method for extracting information from electronic documents, including: learning terms and term variants from a training corpus, wherein the terms and the term variants correspond to a specialized dictionary related to the training corpus; generating a list of negative indicators found in the training corpus; performing a partial match of the terms and the term variants in a set of electronic documents to create initial match results; and performing a negation test using the negative indicators and a positive terms test using the terms and the term variants on the initial match results to remove matches from the initial match results that fail either the negation test or the positive terms test, resulting in final match results.

Abstract: A visual media de-identification system is described. The system includes an image merger and a de-identifying engine. The image merger is configured to merge a sequence of images from a set of visual media data into an averaged image. The de-identifying engine is configured to: bound portions of the averaged image that are determined to be relatively fixed, wherein each bounded portion is identified by a corresponding position in the averaged image; generate a template comprising the bounded portions and the corresponding position for each bounded portion in the averaged image; and de-identify the sequence of images by obfuscating content in the bounded portions.

Abstract: A method for de-identification of visual media data, including: merging a sequence of images from a set of visual media data into an averaged image; bounding portions of the averaged image that are determined to be relatively fixed, wherein each bounded portion is identified by a corresponding position in the averaged image; generating a template comprising the bounded portions and the corresponding position for each bounded portion in the averaged image; and de-identifying the sequence of images by obfuscating content in the bounded portions.

Abstract: A method for monitoring dependent metric streams for anomalies including identifying a plurality of sets of dependent metric streams from a plurality of metric streams of a computer system by measuring an association of the plurality of metric streams using a statistical dependency measure analysis, wherein each set includes a plurality of the dependent metric streams and each metric stream includes a plurality of data, determining a subset of the plurality of sets of dependent metric streams to monitor by selecting a quantity of the sets of dependent metric streams that have a highest statistical dependency, cleaning the data of each set of dependent metric streams of the subset by identifying and removing outlier data, fitting a probability density function to the cleaned data of each set of dependent metric streams of the subset, wherein the probability density function is a likelihood function that provides a likelihood of an occurrence of the cleaned data, determining a detection threshold that is a lowe

Abstract: A method for inferring disease similarity by similarity retrieval of electrocardiogram time-series, comprising: acquiring user ECG waveforms correspondingly depicting many cardiac cycles of the heart of many users stored in a database; pre-processing each of the user ECG waveforms through pre-processing steps to isolate sets of single cardiac cycles corresponding to different heart-rates detected for each of the user ECG waveforms, each single cardiac cycle within the many cardiac cycles of the heart of many users corresponds to one single heart-rate detected.

Abstract: A method for inferring disease similarity by similarity retrieval of electrocardiogram time-series, comprising: acquiring user ECG waveforms correspondingly depicting many cardiac cycles of the heart of many users stored in a database; pre-processing each of the user ECG waveforms through pre-processing steps to isolate sets of single cardiac cycles corresponding to different heart-rates detected for each of the user ECG waveforms, each single cardiac cycle within the many cardiac cycles of the heart of many users corresponds to one single heart-rate detected.