Unitedhealth Group Incorporated

Marilyn L Gordon

Gregory J Boss

Jon Kevin Muse

Rama S Ravindranathan

Guerino Bonetti

Komal Khatri

A controller for an artificial heart enables activity-specific adjustments to the operation of an artificial heart by obtaining sensor data from a plurality of sensors monitoring characteristics of a patient's body, and using the sensor data as input to one or more control parameter models for identifying control parameters to be provided to the artificial heart to adjust the operational parameters of the artificial heart. The controller is in wireless communication with the artificial heart via an application program interface (API)-based communication channel that facilitates communication between the controller and the artificial heart. Moreover, a cloud-based management computing entity may be utilized to train and/or execute one or more models to enable real-time updates to the operational characteristics of the artificial heart to enable the artificial heart to appropriately accommodate activities of the patient.

Artificial heart control systems and methods

Various embodiments of the present disclosure provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for monitoring a user's movement in real-time and providing or augmenting stimulation. For example, various embodiments provide techniques generating movement prediction profiles using movement prediction machine learning models and for use in conjunction with wearable devices.

Movement prediction machine learning models

Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing optimized equipment allocation. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform optimized equipment allocation using at least one of equipment evaluation scoring machine learning models, equipment optimized allocation machine learning models, and equipment history events.

Machine learning techniques for optimized equipment allocation

Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing splinting activity detection. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform splinting activity detection using at least one of splinting activity detection machine learning models, observed inspiration-expiration waveform pattern, and expected inspiration-expiration waveform patterns.

Machine learning techniques for detecting splinting activity

Embodiments of the invention provide apparatuses, systems, and methods for more accurate remote monitoring of a user's body. In some embodiments, a system for monitoring a user's body comprises a wearable device, a video sensor attached at a collar portion of the wearable device, a plurality of audio sensors spaced and attached at a body portion of a wearable device and a controller configured to determine a Jugular Venous Pressure (JVP) of the user, and determine audio characteristics of an output of the plurality of audio sensors to generate an audio heat map corresponding to at least one internal organ of the user.

Jugular venous pressure (JVP) measurement

Embodiments of the invention provide methods for identifying suspect heart or lung sounds and methods for generating an isolated sound sample. In some embodiments, a method for identifying suspect heart or lung sounds comprises obtaining, by processing circuitry, an instance of data samples comprising at least two audio samples and respiratory and/or cardiac cycle data, the audio samples comprising auscultation sounds of an individual, based on the respiratory and/or cardiac cycle data, processing the at least two audio samples to generate a plurality of sound samples, identifying a primary sound sample, generating at least one of a modified secondary sound or a modified baseline sample and combining the modified secondary sound sample or the modified baseline sample with the primary sound sample to generate an isolated sound sample.

Growing community of inventors

Cherry Hill, NJ, United States of America

Marilyn L Gordon