Abstract: A system for an automated analysis of patient-respiratory data including a processor of a respiratory analysis server node configured to host a machine learning (ML) module and connected to at least one patient-entity node over a network and a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: acquire target patient's physiological data from at least one sensor connected to the patient; monitor audio data received from a sensor array, the audio data comprising respiratory sounds originating from the target patient and respiratory sounds originating from persons located in the vicinity of the sensor array; process the audio data to differentiate between the respiratory sounds originating from the target patient and the respiratory sounds originating from the persons located in the vicinity of the sensor array based on at least one property comprising a signal frequency; generate cleaned marked-up audio data based on the processed audio data; pa

Abstract: In various embodiments, a system for cleaning, marking, and/or interpreting physiological data is disclosed. The system includes a memory having instructions stored thereon, and a processor configured to read the instructions to: receive a training data set comprising physiological data including labeled events corresponding to a predetermined portion of the physiological data, generate a trained artificial intelligence (AI) model configured to identify events within device data, and identify at least one physiological event within a target device data set based on the trained AI model. The trained AI model is generated using an iterative training process based on the training data set.

Abstract: A method for monitoring data in a clinical environment comprises receiving information indicative of a number of ambulatory sensing systems in a measurement environment comprising a plurality of ambulatory sensing systems. The method also comprises establishing a communication scheme associated with the measurement environment based on the number of ambulatory sensing systems in the measurement environment. The method further comprises providing, to each ambulatory sensing system, information indicative of the established communication scheme. The method also comprises configuring, in a central data module associated with the measurement environment, parameters for communicating with each respective ambulatory sensing system in accordance with the established control scheme.