Abstract: The invention provides a system and a method thereof for establishing an extubation prediction using a machine learning model capable of obtaining an extubation prediction model and key features used by the extubation prediction model through training and/or verification of a machine learning model, and analyzing key feature data of a patient in real time through the extubation prediction model in order to obtain a possibility of extubation of the patient and its related explanation. Accordingly, the system and the method thereof for establishing the extubation prediction using the machine learning model disclosed in the invention are used as a tool for clinical caregivers to evaluate extubation in order to reduce a possibility of reintubation due to inability to breathe spontaneously after extubation.

Abstract: A respiratory status classifying method is for classifying as one of at least two respiratory statuses and includes an original physiological parameter inputting step, an original chest image inputting step, a characteristic physiological parameter generating step, a characteristic chest image generating step, a training step and a classifier generating step. The characteristic chest image generating step includes processing at least a part of the original chest images, segmenting images of a left lung, a right lung and a heart from each of the original chest images that are processed, and enhancing image data of the images being segmented, so as to generate a plurality of characteristic chest images. The training step includes training two respiratory status classifiers using a plurality of characteristic physiological parameters and the characteristic chest images by at least one machine learning algorithm.

Abstract: A respiratory status classifying method is for classifying as one of at least two respiratory statuses and includes a training's physiological parameter inputting step, a training's chest image inputting step, a characteristic physiological parameter generating step, a characteristic chest image generating step, a training step and a classifier generating step. The characteristic chest image generating step includes processing at least a part of the training's chest images, segmenting images of a left lung, a right lung and a heart from each of the training's chest images that are processed, and enhancing image data of the images being segmented, so as to generate a plurality of characteristic chest images. The training step includes training a plurality of characteristic physiological parameters and the characteristic chest images by at least one machine learning algorithm.