Abstract: Introduced here are computer programs and associated computer-implemented techniques for deriving insights into the health of patients through analysis of audio data generated by electronic stethoscope systems. A diagnostic platform may be responsible for examining the audio data generated by an electronic stethoscope system so as to gain insights into the health of a patient. The diagnostic platform may employ heuristics, algorithms, or models that rely on machine learning or artificial intelligence to perform auscultation in a manner that significantly outperforms traditional approaches that rely on visual analysis by a healthcare professional.

Abstract: Introduced here are computer programs and associated computer-implemented techniques for deriving insights into the health of patients through analysis of audio data generated by electronic stethoscope systems. A diagnostic platform may be responsible for examining the audio data generated by an electronic stethoscope system so as to gain insights into the health of a patient. The diagnostic platform may employ heuristics, algorithms, or models that rely on machine learning or artificial intelligence to perform auscultation in a manner that significantly outperforms traditional approaches that rely on visual analysis by a healthcare professional.

Abstract: Introduced here are computer programs and associated computer-implemented techniques for deriving insights into the health of patients through analysis of audio data generated by electronic stethoscope systems. A diagnostic platform may be responsible for examining the audio data generated by an electronic stethoscope system so as to gain insights into the health of a patient. The diagnostic platform may employ heuristics, algorithms, or models that rely on machine learning or artificial intelligence to perform auscultation in a manner that significantly outperforms traditional approaches that rely on visual analysis by a healthcare professional.

Abstract: Introduced here are computer programs and associated computer-implemented techniques for deriving insights into the health of patients through analysis of audio data generated by electronic stethoscope systems. A diagnostic platform may be responsible for examining the audio data generated by an electronic stethoscope system so as to gain insights into the health of a patient. The diagnostic platform may employ heuristics, algorithms, or models that rely on machine learning or artificial intelligence to perform auscultation in a manner that significantly outperforms traditional approaches that rely on visual analysis by a healthcare professional.

Abstract: Introduced here is a decentralized computing system that is designed to efficiently connect various stakeholders involved in the healthcare system. An application that runs on the decentralized computing system may be responsible for performing activities in a secure manner to accelerate the accumulation and sharing of information among the various stakeholders. These activities may be governed by iterative protocols (e.g., blockchain protocols) that are implemented by the application using the decentralized system.

Abstract: Introduced here are electronic stethoscope systems designed to simultaneously monitor sounds originating from within a body under examination and the ambient environment. An electronic stethoscope system can include one or more input units that are connected to a hub unit. Each input unit may have at least one auscultation microphone and at least one ambient microphone. To improve the quality of sound recorded by an input unit, a processor can apply a noise cancellation algorithm that considers as input the audio data produced by the auscultation microphone(s) and the audio data produced by the ambient microphone(s). The audio data may be digitized directly in the input unit, and then transmitted to the hub unit for synchronization. For example, by examining the audio data produced by the ambient microphone(s), the processor may discover which digital artifacts, if any, should be filtered from the audio data produced by the auscultation microphone(s).

Abstract: Introduced here are electronic stethoscope systems designed to simultaneously monitor sounds originating from within a body under examination and the ambient environment. An electronic stethoscope system can include one or more input units that are connected to a hub unit. Each input unit may have at least one auscultation microphone and at least one ambient microphone. To improve the quality of sound recorded by an input unit, a processor can apply a noise cancellation algorithm that considers as input the audio data produced by the auscultation microphone(s) and the audio data produced by the ambient microphone(s). The audio data may be digitized directly in the input unit, and then transmitted to the hub unit for synchronization. For example, by examining the audio data produced by the ambient microphone(s), the processor may discover which digital artifacts, if any, should be filtered from the audio data produced by the auscultation microphone(s).

Abstract: Introduced here are electronic stethoscope systems designed to simultaneously monitor sounds originating from within a body under examination and the ambient environment. An electronic stethoscope system can include one or more input units that are connected to a hub unit. Each input unit may have at least one auscultation microphone and at least one ambient microphone. To improve the quality of sound recorded by an input unit, a processor can apply a noise cancellation algorithm that considers as input the audio data produced by the auscultation microphone(s) and the audio data produced by the ambient microphone(s). The audio data may be digitized directly in the input unit, and then transmitted to the hub unit for synchronization. For example, by examining the audio data produced by the ambient microphone(s), the processor may discover which digital artifacts, if any, should be filtered from the audio data produced by the auscultation microphone(s).

Abstract: Introduced here are electronic stethoscope systems designed to simultaneously monitor sounds originating from within a body under examination and the ambient environment. An electronic stethoscope system can include one or more input units that are connected to a hub unit. Each input unit may have at least one auscultation microphone and at least one ambient microphone. To improve the quality of sound recorded by an input unit, a processor can apply a noise cancellation algorithm that considers as input the audio data produced by the auscultation microphone(s) and the audio data produced by the ambient microphone(s). The audio data may be digitized directly in the input unit, and then transmitted to the hub unit for synchronization. For example, by examining the audio data produced by the ambient microphone(s), the processor may discover which digital artifacts, if any, should be filtered from the audio data produced by the auscultation microphone(s).

Abstract: Introduced here are electronic stethoscope systems designed to simultaneously monitor sounds originating from within a body under examination and the ambient environment. An electronic stethoscope system can include one or more input units that are connected to a hub unit. Each input unit may have at least one auscultation microphone and at least one ambient microphone. To improve the quality of sound recorded by an input unit, a processor can apply a noise cancellation algorithm that considers as input the audio data produced by the auscultation microphone(s) and the audio data produced by the ambient microphone(s). The audio data may be digitized directly in the input unit, and then transmitted to the hub unit for synchronization. For example, by examining the audio data produced by the ambient microphone(s), the processor may discover which digital artifacts, if any, should be filtered from the audio data produced by the auscultation microphone(s).