Abstract: Aspects of the present disclosure describe systems and methods for predicting an intra-aortic pressure of a patient receiving hemodynamic support from a transvalvular micro-axial heart pump. In some implementations, an intra-aortic pressure time series is derived from measurements of a pressure sensor of the transvalvular micro-axial heart pump and a motor speed time series is derived from a measured back electromotive force of a motor of the transvalvular micro-axial heart pump. Furthermore, in some implementations, machine learning algorithms, such as deep learning, are applied to the intra-aortic pressure and motor speed time series to accurately predict an intra-aortic pressure of the patient. In some implementations, the prediction is short-term (e.g., approximately 5 minutes in advance).

Abstract: Methods and systems for predicting whether a patient is likely to develop post-cardiotomy cardiogenic shock (PCCS) are described. The method includes receiving medical information for a patient, extracting one or more features from the received medical information, providing the one or more features as input to a trained classification model configured to output a risk assessment that the patient is likely to develop PCCS, and outputting an indication of the risk assessment.

Abstract: Aspects of the present disclosure describe systems and methods for predicting an intra-aortic pressure of a patient receiving hemodynamic support from a transvalvular micro-axial heart pump. In some implementations, an intra-aortic pressure time series is derived from measurements of a pressure sensor of the transvalvular micro-axial heart pump and a motor speed time series is derived from a measured back electromotive force of a motor of the transvalvular micro-axial heart pump. Furthermore, in some implementations, machine learning algorithms, such as deep learning, are applied to the intra-aortic pressure and motor speed time series to accurately predict an intra-aortic pressure of the patient. In some implementations, the prediction is short-term (e.g., approximately 5 minutes in advance).

Abstract: Methods and systems are provided that utilize smart hemodynamic support devices positioned in one area of the body, in combination with machine learning to infer and/or detect conditions in other areas of the body operably connected via blood flow.

Abstract: Aspects of the present disclosure describe systems and methods for predicting an intra-aortic pressure of a patient receiving hemodynamic support from a transvalvular micro-axial heart pump. In some implementations, an intra-aortic pressure time series is derived from measurements of a pressure sensor of the transvalvular micro-axial heart pump and a motor speed time series is derived from a measured back electromotive force of a motor of the transvalvular micro-axial heart pump. Furthermore, in some implementations, machine learning algorithms, such as deep learning, are applied to the intra-aortic pressure and motor speed time series to accurately predict an intra-aortic pressure of the patient. In some implementations, the prediction is short-term (e.g., approximately 5 minutes in advance).

Abstract: An electronic device worn on a user includes one or more accelerometers. The one or more accelerometers generate acceleration information based on acceleration experienced by the electronic device. The electronic device further includes a processor and one or more associated memories, and the one or more associate memories include computer program code executable by the processor. The processor, configured by the computer program code, causes the electronic device to process the acceleration information to extract features from the acceleration information. The processor, configured by the computer program code, further causes the electronic device to process the features to determine the location of the electronic device on the user.

Abstract: Aspects of the present disclosure describe systems and methods for predicting an intra-aortic pressure of a patient receiving hemodynamic support from a transvalvular micro-axial heart pump. In some implementations, an intra-aortic pressure time series is derived from measurements of a pressure sensor of the transvalvular micro-axial heart pump and a motor speed time series is derived from a measured back electromotive force of a motor of the transvalvular micro-axial heart pump. Furthermore, in some implementations, machine learning algorithms, such as deep learning, are applied to the intra-aortic pressure and motor speed time series to accurately predict an intra-aortic pressure of the patient. In some implementations, the prediction is short-term (e.g., approximately 5 minutes in advance).

Abstract: An electronic device worn on a user includes one or more accelerometers. The one or more accelerometers generate acceleration information based on acceleration experienced by the electronic device. The electronic device further includes a processor and one or more associated memories, and the one or more associate memories include computer program code executable by the processor. The processor, configured by the computer program code, causes the electronic device to process the acceleration information to extract features from the acceleration information. The processor, configured by the computer program code, further causes the electronic device to process the features to determine the location of the electronic device on the user.

Abstract: An electronic device worn on a user includes one or more accelerometers. The one or more accelerometers generate acceleration information based on acceleration experienced by the electronic device. The electronic device further includes a processor and one or more associated memories, and the one or more associate memories include computer program code executable by the processor. The processor, configured by the computer program code, causes the electronic device to process the acceleration information to extract features from the acceleration information. The processor, configured by the computer program code, further causes the electronic device to process the features to determine the location of the electronic device on the user.

Abstract: An electronic device worn on a user includes one or more accelerometers. The one or more accelerometers generate acceleration information based on acceleration experienced by the electronic device. The electronic device further includes a processor and one or more associated memories, and the one or more associate memories include computer program code executable by the processor. The processor, configured by the computer program code, causes the electronic device to process the acceleration information to extract features from the acceleration information. The processor, configured by the computer program code, further causes the electronic device to process the features to determine the location of the electronic device on the user.