Abstract: A method and system for automatically inferring a subject's body position in a two-dimensional image produced by a medical-imaging system are disclosed. The image is labeled with a body position selected from a semantically meaningful set of candidate positions sequenced in order of their relative locations in a subject's body. A processor performs procedures that each identify a class of image features related to pixel intensity, such as a histogram of gradients, local binary patterns, or Haar-like features. A second set of procedures employs applications of a pretrained convolutional neural network that has learned to recognize features of a specific class of medical images. The results of both types of procedures are then mapped by a pretrained support-vector machine onto candidate image labels, which are mathematically combined into a single, semantically meaningful, label most likely to identify a body position of the subject shown by the image.

Abstract: Mechanisms are provided to implement an automated echocardiograph measurement extraction system. The automated echocardiograph measurement extraction system receives medical imaging data comprising one or more medical images and inputs the one or more medical images into a deep learning network. The deep learning network automatically processes the one or more medical images to generate an extracted echocardiograph measurement vector output comprising one or more values for echocardiograph measurements extracted from the one or more medical images. The deep learning network outputs the extracted echocardiograph measurement vector output to a medical image viewer.

Abstract: Mechanisms are provided to implement a patient summary generation engine with deduplication of instances of medical concepts. The patient summary generation engine parses a patient electronic medical record (EMR) to extract a plurality of instances of a medical concept, at least two of which utilize different representations of the medical concept. The patient summary generation engine performs a similarity analysis between each of the instances of a medical concept to thereby calculate, for a plurality of combinations of instances of the medical concept, a similarity metric value. The patient summary generation engine clusters the instances of the medical concept based on the calculated similarity metric values for each combination of instances in the plurality of combinations of instances of the medical concept to thereby generate one or more clusters, and select a representative instance of the medical concept from each cluster in the one or more clusters.

Abstract: Mechanisms are provided to implement a patient summary generation engine with deduplication of instances of medical concepts. The patient summary generation engine parses a patient electronic medical record (EMR) to extract a plurality of instances of a medical concept, at least two of which utilize different representations of the medical concept. The patient summary generation engine performs a similarity analysis between each of the instances of a medical concept to thereby calculate, for a plurality of combinations of instances of the medical concept, a similarity metric value. The patient summary generation engine clusters the instances of the medical concept based on the calculated similarity metric values for each combination of instances in the plurality of combinations of instances of the medical concept to thereby generate one or more clusters, and select a representative instance of the medical concept from each cluster in the one or more clusters.

Abstract: Mechanisms are provided to implement an automated medical image processing pipeline selection (MIPPS) system. The MIPPS system receives medical image data associated with a patient electronic medical record and analyzes the medical image data to extract evidence data comprising characteristics of one or more medical images in the medical image data indicative of a medical image processing pipeline to select for processing the one or more medical images. The evidence data is provided to a machine learning model of the MIPPS system which selects a medical image processing pipeline based on a machine learning based analysis of the evidence data. The selected medical image processing pipeline processes the medical image data to generate a results output.

Abstract: Mechanisms are provided to implement a multi-layer analytics framework. The multi-layer analytics framework obtains a plurality of analytics from one or more analytics source computing systems, at least two analytics being written in different computer programming languages. The multi-layer analytics framework applies a wrapper to each of the analytics in the plurality of analytics to thereby generate wrapped analytics. The wrapper provides a unified interface for executing the analytics in the plurality of analytics regardless of the particular computer programming language used to create the analytics. The multi-layer analytics framework registers the wrapped analytics in an analytics registry, and executes an analytics pipeline comprising wrapped analytics in the analytics registry to perform an analytics operation based on the unified interface of the wrappers of the wrapped analytics.

Abstract: Mechanisms are provided to implement a machine learning training model. The machine learning training model trains an image generator of a generative adversarial network (GAN) to generate medical images approximating actual medical images. The machine learning training model augments a set of training medical images to include one or more generated medical images generated by the image generator of the GAN. The machine learning training model trains a machine learning model based on the augmented set of training medical images to identify anomalies in medical images. The trained machine learning model is applied to new medical image inputs to classify the medical images as having an anomaly or not.

Abstract: A mechanism is provided for implement a discrepancy detection mechanism for detecting discrepancies between clinical notes and administrative records. Clinical concepts are extracted from the clinical notes and the administrative records in a patient's electronic medical records (EMRs). The extracted clinical concepts are filtered based on semantic type information to identify concepts that reference diseases or syndromes while also removing negated instances. Utilizing the positive mentions of diseases in clinical notes, the positive mentions of diseases or syndromes in the clinical notes are compared against each positive entry in the administrative records. A discrepancy summary is then generated for diseases or syndromes that failed to translate correctly from clinical notes to the administrative records in the patient's EMRs.

Abstract: A mechanism is provided for implement a discrepancy detection mechanism for detecting discrepancies between clinical notes and administrative records. Clinical concepts are extracted from the clinical notes and the administrative records in a patient's electronic medical records (EMRs). The extracted clinical concepts are filtered based on semantic type information to identify concepts that reference diseases or syndromes while also removing negated instances. Utilizing the positive mentions of diseases in clinical notes, the positive mentions of diseases or syndromes in the clinical notes are compared against each positive entry in the administrative records. A discrepancy summary is then generated for diseases or syndromes that failed to translate correctly from clinical notes to the administrative records in the patient's EMRs.

Abstract: An automatic extraction of disease-specific features from Doppler images to help diagnose valvular diseases is provided. The method includes the steps of obtaining a raw Doppler image from a series of images of an echocardiogram, isolating a region of interest from the raw Doppler image, the region of interest including a Doppler image and an ECG signal, and depicting at least one heart cycle, determining a velocity envelope of the Doppler image in the region of interest, extracting the ECG signal to synchronize the ECG signal with the Doppler image over the at least one heart cycle, within the region of interest, calculating a value of a clinical feature based on the extracted ECG signal synchronized with the velocity envelope, and comparing the value of the clinical feature with clinical guidelines associated with the clinical feature to determine a diagnosis of a disease.

Abstract: A multi-layer analytics framework is provided that obtains a plurality of analytics from one or more analytics source computing systems. The framework applies a wrapper to each of the analytics, where the wrapper provides a unified interface for executing the analytics regardless of the particular computer programming language used to create the analytics. The framework registers the wrapped analytics in an analytics registry, receives a request to perform an analytics operation on an input dataset, from a request computing system, and automatically generates an analytics pipeline comprising a plurality of wrapped analytics retrieved from the analytics registry. The framework executes the analytics pipeline and returns results of executing the analytics pipeline to the requestor computing system.

Abstract: Mechanisms are provided to implement a machine learning training model. The machine learning training model trains an image generator of a generative adversarial network (GAN) to generate medical images approximating actual medical images. The machine learning training model augments a set of training medical images to include one or more generated medical images generated by the image generator of the GAN. The machine learning training model trains a machine learning model based on the augmented set of training medical images to identify anomalies in medical images. The trained machine learning model is applied to new medical image inputs to classify the medical images as having an anomaly or not.

Abstract: Mechanisms are provided to implement a multi-layer analytics framework. The multi-layer analytics framework obtains a plurality of analytics from one or more analytics source computing systems, at least two analytics being written in different computer programming languages. The multi-layer analytics framework applies a wrapper to each of the analytics in the plurality of analytics to thereby generate wrapped analytics. The wrapper provides a unified interface for executing the analytics in the plurality of analytics regardless of the particular computer programming language used to create the analytics. The multi-layer analytics framework registers the wrapped analytics in an analytics registry, and executes an analytics pipeline comprising wrapped analytics in the analytics registry to perform an analytics operation based on the unified interface of the wrappers of the wrapped analytics.

Abstract: Management of collections of medical documents is provided. In various embodiments, search criteria are specified for one or more datastore. Location information for a plurality of medical documents (e.g. images, textual documents, time series data, etc.) is retrieved from the one or more datastore. The location information for the plurality of medical documents is aggregated into a virtual collection. The virtual collection is indexed by metadata of the virtual collection.

Abstract: A mechanism is provided in a data processing system to implement a data mapping engine for transparent and declarative translation of search queries on documents to queries on relational data. The data mapping engine receives a query from a search framework for a target database and translates the query to a target schema based on a mapping definition data structure to form a translated query. The mapping definition data structure declaratively maps between a source schema of the search framework to a target schema of a target database. The data mapping engine sends the translated query to the target database and receives a response from the target database. The data mapping engine translates the response to the source schema based on the mapping definition data structure to form a translated response and sends the translated response to the search framework.

Abstract: Mechanisms are provided to implement an automated medical image processing pipeline selection (MIPPS) system. The MIPPS system receives medical image data associated with a patient electronic medical record and analyzes the medical image data to extract evidence data comprising characteristics of one or more medical images in the medical image data indicative of a medical image processing pipeline to select for processing the one or more medical images. The evidence data is provided to a machine learning model of the MIPPS system which selects a medical image processing pipeline based on a machine learning based analysis of the evidence data. The selected medical image processing pipeline processes the medical image data to generate a results output.

Abstract: Mechanisms are provided to implement a multi-layer analytics framework. The multi-layer analytics framework obtains a plurality of analytics from one or more analytics source computing systems, at least two analytics being written in different computer programming languages. The multi-layer analytics framework applies a wrapper to each of the analytics in the plurality of analytics to thereby generate wrapped analytics. The wrapper provides a unified interface for executing the analytics in the plurality of analytics regardless of the particular computer programming language used to create the analytics. The multi-layer analytics framework registers the wrapped analytics in an analytics registry, and executes an analytics pipeline comprising wrapped analytics in the analytics registry to perform an analytics operation based on the unified interface of the wrappers of the wrapped analytics.

Abstract: Mechanisms are provided to implement a patient summary generation engine with deduplication of instances of medical concepts. The patient summary generation engine parses a patient electronic medical record (EMR) to extract a plurality of instances of a medical concept, at least two of which utilize different representations of the medical concept. The patient summary generation engine performs a similarity analysis between each of the instances of a medical concept to thereby calculate, for a plurality of combinations of instances of the medical concept, a similarity metric value. The patient summary generation engine clusters the instances of the medical concept based on the calculated similarity metric values for each combination of instances in the plurality of combinations of instances of the medical concept to thereby generate one or more clusters, and select a representative instance of the medical concept from each cluster in the one or more clusters.

Abstract: A mechanism is provided in a data processing system comprising a processor and a memory, the memory comprising instructions executed by the processor to specifically configure the processor to implement a multi-atlas segmentation engine. An offline registration component performs registration of a plurality of atlases with a set of image templates to thereby generate and store, in a first registration storage device, a plurality of offline registrations. The atlases are annotated training medical images and the image templates are non-annotated medical images. The multi-atlas segmentation engine receives a target image. An image selection component selects a subset of image templates in the set of image templates based on the target image. An online registration component performs registration of the subset of image templates with the target image to generate a plurality of online registrations.

Abstract: Mechanisms are provided to implement an automated medical image processing pipeline selection (MIPPS) system. The MIPPS system receives medical image data associated with a patient electronic medical record and analyzes the medical image data to extract evidence data comprising characteristics of one or more medical images in the medical image data indicative of a medical image processing pipeline to select for processing the one or more medical images. The evidence data is provided to a machine learning model of the MIPPS system which selects a medical image processing pipeline based on a machine learning based analysis of the evidence data. The selected medical image processing pipeline processes the medical image data to generate a results output.