Abstract: Systems and methods for training a machine learning based natural language processor to process specialized language terms. A training corpus of labeled specialized language terms associated with a specialized subject area is assembled. A generated database query corresponding to a question comprising at least one specialized language term within the set of labeled specialized language terms is created with a machine learning based natural language processing process. A cost function is determined based on differences between the generated database query and contents in a knowledge graph mapping terms describing characteristics of entities associated with the specialized subject area. The natural language processing process is refined based on feeding back the cost function to create a refined natural language process.

Abstract: A system for marking cardiac time intervals from heart valve signals includes a non-invasive sensor unit for capturing electrical signals and composite vibration objects, a memory containing computer instructions, and one or more processors coupled to the memory. The one or more processors causes the one or more processors to perform operations including separating a plurality of individual heart vibration events into heart valve signals from the composite vibration objects, and marking cardiac time interval from the heart valve signals by detecting individual heartbeats and processing cumulative energy within the individual heartbeat to set a threshold to set a marking point.

Abstract: A system and method for monitoring and diagnosing heart conditions may include capturing and processing a composite heart signal and isolating individual components of such a composite signal. The disclosed techniques of measuring hemodynamic parameters may extract information contained in cardio-pulmonic vibrations. In operation, a system and method may separate different vibration signals along with event time information with respect to a synchronous electrocardiogram signal, and may provide for measurement of hemodynamic parameters from these separated signals.

Abstract: A system for identifying heart valve signals includes a non-invasive sensor unit for capturing electrical signals and composite vibration objects, a memory containing computer instructions, and one or more processors coupled to the memory, where the one or more processors are configured to perform the steps of separating a plurality of individual heart vibration events from the composite vibration objects by using at least one among bin-wise clustering and permutation alignment, or non-negative matrix factorization, or deep belief networks and tagging the plurality of individual heart vibration events using at least one among principal component analysis, Gabor filtering, generalized cross correlation, phase transform, smoothed coherent transformation, Roth correlation or band filtering.

Abstract: A system for marking cardiac time intervals from heart valve signals includes a non-invasive sensor unit for capturing electrical signals and composite vibration objects, a memory containing computer instructions, and one or more processors coupled to the memory. The one or more processors causes the one or more processors to perform operations including separating a plurality of individual heart vibration events into heart valve signals from the composite vibration objects, and marking cardiac time interval from the heart valve signals by detecting individual heartbeats and processing cumulative energy within the individual heartbeat to set a threshold to set a marking point.