METHOD AND SYSTEM FOR AI-BASED ANALYSIS OF RESPIRATORY CONDITIONS

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; parse the target patient's physiological data and the cleaned and marked audio data to derive a set of classifying features; query a patients' database to retrieve local historical respiratory analysis'-related data based on the set of classifying features; generate at least one classifier vector based on the set of classifying features and the local historical respiratory analysis'-related data; provide the at least one classifier vector to the ML module configured to generate a predictive model for producing a set of respiratory analysis parameters; and generate at least one respiratory analysis verdict based on the set of respiratory analysis parameters.