Abstract: Real-time alerts and transmission of selected signal samples is disclosed. One example is a system including a base facility linked to a production station with an alerting system to perform anomaly analysis utilizing an anomaly model. A receiver at the base facility receives, from the production station, a selection of signal samples based on the anomaly analysis, where the received selection is optimized at the production station to be substantially relevant to an update of a statistical model while adhering to a dynamic capacity limitation of the production station. The statistical model is maintained at the base facility and incorporates features related to the production station. A management system at the base facility updates the statistical model based on the received selection, optionally derives an updated anomaly model based on the statistical model, and optionally transmits the updated anomaly model to the production station.

Abstract: Techniques are described that facilitate online monitoring of clinical data streams in association with detecting missing data and other suspicious data deviations. According to an embodiment, a computer implemented comprises receiving, by a system comprising a processor, a data stream from a plurality of different clinical data information systems configured to report defined clinical events within the data stream and recording arrival times of received events of the defined clinical events. The method further comprises detecting, by the system, data failure events associated with the data stream based on time differences between the arrival times for defined clinical events of the same type and estimated probabilities that the time differences are expected, wherein the detecting comprises estimating the probabilities that the time differences are expected using time-to-event models developed for each of the defined clinical events of the same type.

Abstract: Techniques are described that employ a machine learning approach for detecting data discrepancies during clinical data integration. In an embodiment, a computer implemented method comprises receiving historical clinical data messages converted from a native format to a target format via a mapping function that maps different sets of historical data elements included in the historical clinical data messages into defined data description paths, and training anomaly detection models for each of the defined data description paths to characterize normal characteristics of the different sets of historical data elements for each of the defined data description path. The method further comprises receiving new clinical data messages converted from the native format or to the target format via the mapping function, and detecting abnormal characteristics of different sets of new data elements for corresponding data description paths of the defined data description paths using the anomaly detection models.

Abstract: Methods and systems are provided for tracking anatomical features across multiple images. One example method includes outputting, for display on a display device, an annotation indicative of a first location of an identified anatomical feature of a first ultrasound image, the annotation generated based on a first output of a model and outputting, for display on the display device, an adjusted annotation based on a second output of the model, the second output of the model generated based on a second ultrasound image and further based on the first output of the model, the adjusted annotation indicative of a second location of the identified anatomical feature in the second ultrasound image.

Abstract: Various systems and devices are provided for facilitating communication in a healthcare setting. In one example, a system for providing medical information can include a processor that, in response to executing machine-readable instructions, can generate a patient dashboard comprising the medical information for a patient and a patient communication thread. The processor can also output the patient dashboard to a display, wherein the patient dashboard further comprises a first link proximate to a displayed parameter from the medical information and the processor can obtain communication among care providers monitoring the patient and virtual healthcare assistants configured to communicate on the communication thread. The processor can also output the communication from the care providers, the virtual healthcare assistances, or any combination thereof, in the patient communication thread of the patient dashboard provided by the display.

Abstract: Various systems and devices are provided for facilitating communication in a healthcare setting. In one example, a system includes a display and a computing device operably coupled to the display. The computing device stores instructions executable to output, to the display, a patient-specific dashboard including medical information specific to the patient, the dashboard further including a displayed link to a patient-specific communication thread; and responsive to a user selecting the link, output to the display a portion of the communication thread, where the communication thread includes communication among one or more care providers monitoring the patient and one or more virtual healthcare assistants.

Abstract: Real-time alerts and transmission of selected signal samples is disclosed. One example is a system including a base facility linked to a production station with an alerting system to perform anomaly analysis utilizing an anomaly model. A receiver at the base facility receives, from the production station, a selection of signal samples based on the anomaly analysis, where the received selection is optimized at the production station to be substantially relevant to an update of a statistical model while adhering to a dynamic capacity limitation of the production station. The statistical model is maintained at the base facility and incorporates features related to the production station. A management system at the base facility updates the statistical model based on the received selection, optionally derives an updated anomaly model based on the statistical model, and optionally transmits the updated anomaly model to the production station.

Abstract: Real-time alerts and transmission of selected signal samples is disclosed. One example is a system including a base facility linked to a production station with an alerting system to perform anomaly analysis utilizing an anomaly model. A receiver at the base facility receives, from the production station, a selection of signal samples based on the anomaly analysis, where the received selection is optimized at the production station to be substantially relevant to an update of a statistical model while adhering to a dynamic capacity limitation of the production station. The statistical model is maintained at the base facility and incorporates features related to the production station. A management system at the base facility updates the statistical model based on the received selection, optionally derives an updated anomaly model based on the statistical model, and optionally transmits the updated anomaly model to the production station.

Abstract: Methods and systems are provided for tracking anatomical features across multiple images. One example method includes outputting, for display on a display device, an annotation indicative of a first location of an identified anatomical feature of a first ultrasound image, the annotation generated based on a first output of a model and outputting, for display on the display device, an adjusted annotation based on a second output of the model, the second output of the model generated based on a second ultrasound image and further based on the first output of the model, the adjusted annotation indicative of a second location of the identified anatomical feature in the second ultrasound image.

Abstract: A system and method for analyzing ultrasound scenes to provide needle guidance and warnings is provided. The method includes acquiring, by an ultrasound system, an ultrasound image. The method includes segmenting, by at least one processor, the ultrasound image to identify a plurality of structures in the ultrasound image. The method includes highlighting, by the at least one processor, the plurality of structures in the ultrasound image to create a highlighted ultrasound image. The method includes presenting, by the at least one processor, the highlighted ultrasound image at a display system. The method includes determining, by the at least one processor, a distance between at least two of the plurality of structures in the ultrasound image. The method includes providing, by the at least one processor, a warning if the distance between the at least two of the plurality of structures in the ultrasound image is less than a threshold.

Abstract: A system and method for facilitating interaction by an ultrasound operator with an artificial intelligence segmentation module configured to identify and track biological and/or artificial structures in ultrasound images is provided. The method includes acquiring an ultrasound image and segmenting the ultrasound image with artificial intelligence to identify structure(s) in the ultrasound image. The method includes labeling the structure(s) in the ultrasound image to create a labeled ultrasound image. The method includes presenting the labeled ultrasound image at a display system. The method includes receiving a user selection of at least one target corresponding with at least one labeled structure. The method includes tracking the selected target(s) by identifying the selected target(s) in subsequently acquired ultrasound images.

Abstract: A method, a computing system, and a non-transitory machine readable storage medium containing instructions for identifying relationships between entities are provided. In an example, the method includes receiving a query. The query specifies a first computing entity, a second computing entity, and a window of time. A data structure is queried based on the query to identify a set of relationship instances each corresponding to a relationship between the first computing entity and the second computing entity during the window of time. A representation of the first computing entity, the second computing entity, and the set of relationship instances is provided at a user interface.

Abstract: Various systems and devices are provided for facilitating communication in a healthcare setting. In one example, a system includes a display and a computing device operably coupled to the display. The computing device stores instructions executable to output, to the display, a patient-specific dashboard including medical information specific to the patient, the dashboard further including a displayed link to a patient-specific communication thread; and responsive to a user selecting the link, output to the display a portion of the communication thread, where the communication thread includes communication among one or more care providers monitoring the patient and one or more virtual healthcare assistants.

Abstract: In one implementation, a plurality of signature vectors from a multi-dimensional representation of a graphical object is generated. Each of the signature vectors comprises attributes that vary little in response to changes in shape, size, orientation, and visual layer appearance of the graphical object, and each of the signature vectors includes attributes based on operations of integration, differentiation, and transforms on the multi-dimensional representation of the graphical object. Each signature vector is composited into multiple portions from the plurality of signature vectors to define an appearance-invariant signature of the graphical object.

Abstract: Real-time alerts and transmission of selected signal samples is disclosed. One example is a system including a base facility linked to a production station with an alerting system to perform anomaly analysis utilizing an anomaly model. A receiver at the base facility receives, from the production station, a selection of signal samples based on the anomaly analysis, where the received selection is optimized at the production station to be substantially relevant to an update of a statistical model while adhering to a dynamic capacity limitation of the production station. The statistical model is maintained at the base facility and incorporates features related to the production station. A management system at the base facility updates the statistical model based on the received selection, optionally derives an updated anomaly model based on the statistical model, and optionally transmits the updated anomaly model to the production station.

Abstract: In one implementation, an image-based automation process includes identifying a graphical object of a graphical user interface and performing an action relative to the graphical object at the graphical user interface. The identifying is based on an image including the graphical object.

Abstract: A method, a computing system, and a non-transitory machine readable storage medium containing instructions for identifying relationships between entities are provided. In an example, the method includes receiving a query. The query specifies a first computing entity, a second computing entity, and a window of time. A data structure is queried based on the query to identify a set of relationship instances each corresponding to a relationship between the first computing entity and the second computing entity during the window of time. A representation of the first computing entity, the second computing entity, and the set of relationship instances is provided at a user interface.

Abstract: According to an example, intent based clustering may include generating a plurality of clusters based on an analysis of categories of features of data used to generate the clusters with respect to an order of each of the features by determining whether a number of samples of the data for a category of the categories meets a specified criterion.

Abstract: A non-transitory storage device containing software than, when executed by a processor, causes the processor to generate a projection set of polynomials based on a projection of a space linear combination of candidate polynomials of degree d on polynomials of degree less than d that do not evaluate to less than a threshold on a set of points. The software also causes the processor to compute the singular value decomposition of a matrix containing the difference between candidate polynomials evaluated on the points and the projection set of polynomials evaluated on the points, and to partition the polynomials resulting from the singular value decomposition based on a threshold.

Abstract: A method, system and storage device are generally directed to determining for each of a plurality of data points, a neighborhood of data points about each such data point. For each such neighborhood of data points, a projection set of polynomials is generated based on candidate polynomials. The projection set of polynomials evaluated on the neighborhood of data points is subtracted from the plurality of candidate polynomials evaluated on the neighborhood of data points to generate a subtraction matrix of evaluated resulting polynomials. The singular value decomposition of the subtraction matrix is then computed. The resulting polynomials are clustered into multiple clusters and then partitioned based on a threshold.