Abstract: A method for determining cardiology disease risk from electrocardiogram trace data and clinical data includes receiving electrocardiogram trace data associated with a patient, receiving the patient's clinical data, providing both sets of data to a trained machine learning composite model that is trained to evaluate the data with respect to each disease of a set of cardiology diseases including three or more of cardiac amyloidosis, aortic stenosis, aortic regurgitation, mitral stenosis, mitral regurgitation, tricuspid regurgitation, abnormal reduced ejection fraction, or abnormal interventricular septal thickness, generating, by the model and based on the evaluation, a composite risk score reflecting a likelihood of the patient being diagnosed with one or more of the cardiology diseases within a predetermined period of time from when the electrocardiogram trace data was generated, and outputting the composite risk score to at least one of a memory or a display.

Abstract: A method for determining cardiology disease risk from electrocardiogram trace data and clinical data includes receiving electrocardiogram trace data associated with a patient, receiving the patient's clinical data, providing both sets of data to a trained machine learning composite model that is trained to evaluate the data with respect to each disease of a set of cardiology diseases including three or more of cardiac amyloidosis, aortic stenosis, aortic regurgitation, mitral stenosis, mitral regurgitation, tricuspid regurgitation, abnormal reduced ejection fraction, or abnormal interventricular septal thickness, generating, by the model and based on the evaluation, a composite risk score reflecting a likelihood of the patient being diagnosed with one or more of the cardiology diseases within a predetermined period of time from when the electrocardiogram trace data was generated, and outputting the composite risk score to at least one of a memory or a display.

Abstract: A method and system for determining cardiac disease risk from electrocardiogram trace data is provided. The method includes receiving electrocardiogram trace data associated with a patient, the electrocardiogram trace data having an electrocardiogram configuration including a plurality of leads. One or more leads of the plurality of leads that are derivable from a combination of other leads of the plurality of leads are identified, and a portion of the electrocardiogram trace data does not include electrocardiogram trace data of the one or more leads. The portion of the electrocardiogram data is provided to a trained machine learning model, to evaluate the portion of the electrocardiogram trace data with respect to one or more cardiac disease states. A risk score reflecting a likelihood of the patient being diagnosed with a cardiac disease state within a predetermined period of time is generated by the trained machine learning model based on the evaluation.

Abstract: A method and system for predicting the likelihood that a patient will suffer from a cardiac event is provided. The method includes receiving electrocardiogram data associated with the patient, providing at least a portion of the electrocardiogram data to a trained model, receiving a risk score indicative of the likelihood the patient will suffer from the cardiac event within a predetermined period of time from when the electrocardiogram data was generated, and outputting the risk score to at least one of a memory or a display for viewing by a medical practitioner or healthcare administrator. The system includes at least one processor executing instructions to carry out the steps of the method.

Abstract: A method and system for predicting the likelihood that a patient will suffer from a cardiac event is provided. The method includes receiving electrocardiogram data associated with the patient, providing at least a portion of the electrocardiogram data to a trained model, receiving a risk score indicative of the likelihood the patient will suffer from the cardiac event within a predetermined period of time from when the electrocardiogram data was generated, and outputting the risk score to at least one of a memory or a display for viewing by a medical practitioner or healthcare administrator. The system includes at least one processor executing instructions to carry out the steps of the method.

Abstract: A method includes the step of receiving electrocardiogram (ECG) data associated with a plurality of patients and an electrocardiogram configuration including a plurality of leads and a time interval. The electrocardiogram data includes, for each lead included in the plurality of leads, voltage data associated with at least a portion of the time interval. The method also includes training an artificial intelligence model on the ECG data, tuning the artificial intelligence model using data from a device having fewer leads than the plurality of leads, and evaluating the artificial intelligence model on additional data received from the ECG data.

Abstract: A method and system for predicting the likelihood that a patient will suffer from a cardiac event is provided. The method includes receiving electrocardiogram data associated with the patient, providing at least a portion of the electrocardiogram data to a trained model, receiving a risk score indicative of the likelihood the patient will suffer from the cardiac event within a predetermined period of time from when the electrocardiogram data was generated, and outputting the risk score to at least one of a memory or a display for viewing by a medical practitioner or healthcare administrator. The system includes at least one processor executing instructions to carry out the steps of the method.

Abstract: A method for determining cardiology disease risk from electrocardiogram trace data and clinical data includes receiving electrocardiogram trace data associated with a patient, receiving the patient's clinical data, providing both sets of data to a trained machine learning composite model that is trained to evaluate the data with respect to each disease of a set of cardiology diseases including three or more of cardiac amyloidosis, aortic stenosis, aortic regurgitation, mitral stenosis, mitral regurgitation, tricuspid regurgitation, abnormal reduced ejection fraction, or abnormal interventricular septal thickness, generating, by the model and based on the evaluation, a composite risk score reflecting a likelihood of the patient being diagnosed with one or more of the cardiology diseases within a predetermined period of time from when the electrocardiogram trace data was generated, and outputting the composite risk score to at least one of a memory or a display.

Abstract: A method and system for predicting the likelihood that a patient will suffer from a cardiac event is provided. The method includes receiving electrocardiogram data associated with the patient, providing at least a portion of the electrocardiogram data to a trained model, receiving a risk score indicative of the likelihood the patient will suffer from the cardiac event within a predetermined period of time from when the electrocardiogram data was generated, and outputting the risk score to at least one of a memory or a display for viewing by a medical practitioner or healthcare administrator. The system includes at least one processor executing instructions to carry out the steps of the method.

Abstract: Embodiments of the present invention are directed towards methods and systems for providing an enhanced telepresence experience to users participating in a videoconferencing session (VCS). In the embodiments a camera is configured and arranged to capture image data covering objects within a substantial portion of the field of view (FOV) of a display device, without capturing image data encoding images displayed on the display device. That is, the camera's FOV is aligned with the display device's FOV. As such, the camera captures image data encoding images in a substantial portion of the display's FOV. According, users within a VCS may approach their display without falling outside their camera's FOV. This provides an enhanced telepresence experience, where the users may interact with each other through what appears to be a transparent window or barrier.

Abstract: Embodiments of the present invention are directed towards methods and systems for providing an enhanced telepresence experience to users participating in a videoconferencing session (VCS). In the embodiments a camera is configured and arranged to capture image data covering objects within a substantial portion of the field of view (FOV) of a display device, without capturing image data encoding images displayed on the display device. That is, the camera's FOV is aligned with the display device's FOV. As such, the camera captures image data encoding images in a substantial portion of the display's FOV. According, users within a VCS may approach their display without falling outside their camera's FOV. This provides an enhanced telepresence experience, where the users may interact with each other through what appears to be a transparent window or barrier.

Abstract: Embodiments of the present invention are directed towards methods and systems for providing an enhanced telepresence experience to users participating in a videoconferencing session (VCS). In the embodiments a camera is configured and arranged to capture image data covering objects within a substantial portion of the field of view (FOV) of a display device, without capturing image data encoding images displayed on the display device. That is, the camera's FOV is aligned with the display device's FOV. As such, the camera captures image data encoding images in a substantial portion of the display's FOV. According, users within a VCS may approach their display without falling outside their camera's FOV. This provides an enhanced telepresence experience, where the users may interact with each other through what appears to be a transparent window or barrier.

Abstract: A method and apparatus for quantitatively measuring differences between portions of a multivariate, multi-dimensional sample distribution, may comprise summarizing the data by dividing the data into clusters each having a signature representative of a position of the cluster and a fraction of the entire distribution within the cluster; matching a plurality of first supplier signatures to a respective one of a plurality of second receiver signatures using a cost factor indicative of the separation between first signature elements and second signature elements; and determining a measurement of the work required to transform the first signature to the second signature. The step of determining a measurement of the work may comprise applying the earth mover distance (“EMD”) algorithm between the first signature or elements of the first signature and the respective second signatures or elements of the respective second signature.

Abstract: Embodiments of the present invention are directed towards methods and systems for providing an enhanced telepresence experience to users participating in a videoconferencing session (VCS). In the embodiments a camera is configured and arranged to capture image data covering objects within a substantial portion of the field of view (FOV) of a display device, without capturing image data encoding images displayed on the display device. That is, the camera's FOV is aligned with the display device's FOV. As such, the camera captures image data encoding images in a substantial portion of the display's FOV. According, users within a VCS may approach their display without falling outside their camera's FOV. This provides an enhanced telepresence experience, where the users may interact with each other through what appears to be a transparent window or barrier.

Abstract: Medical guidelines are generated based on the history of the medical records for a large patient population, which includes creating a patient trajectory graph from the records including nodes and edges by automatically clustering patients based on relevant the patients' features included in their medical records. The nodes are scored based on the time patients remain with the nodes and desirability of any associated outcomes, resulting in edge scores derived from the scores of the edge-connected nodes. Top ranked interventions obtained from the edge scores that evaluates whether a transition from one node to another is better or worse are included in the generated medical guidelines. Additionally, effect sizes and confidence intervals of medical treatments for a pre-defined patient population are estimated by using the patients' medical records and dividing the population in an exposed and non-exposed group. Estimates are based on match choices between exposed and non-exposed patients.

Abstract: A method and apparatus for quantitatively measuring differences between portions of a multivariate, multi-dimensional sample distribution, may comprise summarizing the data by dividing the data into clusters each having a signature representative of a position of the cluster and a fraction of the entire distribution within the cluster; matching a plurality of first supplier signatures to a respective one of a plurality of second receiver signatures using a cost factor indicative of the separation between first signature elements and second signature elements; and determining a measurement of the work required to transform the first signature to the second signature. The step of determining a measurement of the work may comprise applying the earth mover distance (“EMD”) algorithm between the first signature or elements of the first signature and the respective second signatures or elements of the respective second signature.

Abstract: The present invention provides computer program products, systems, and related methods of coordinating a knowledge base.

Abstract: The present invention provides computer program products, systems, and related methods of coordinating a knowledge base.