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: An acute kidney injury predicting system and a method thereof are proposed. A processor reads the data to be tested, the detection data, the machine learning algorithm and the risk probability comparison table from a main memory. The processor trains the detection data according to the machine learning algorithm to generate an acute kidney injury prediction model, and inputs the data to be tested into the acute kidney injury prediction model to generate an acute kidney injury characteristic risk probability and a data sequence table. The data sequence table lists the data to be tested in sequence according to a proportion of each of the data to be tested in the acute kidney injury characteristics. The processor selects one of the medical treatment data from the risk probability comparison table according to the acute kidney injury characteristic risk probability.

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 bloodstream infection predicting system and a method thereof are proposed. The memory unit stores a plurality of historical medical data, the real-time data to be tested and a machine learning algorithm. The processor is configured to implement a bloodstream infection predicting method. The bloodstream infection predicting method includes reading the historical medical data from the memory unit, training the historical medical data to generate a bloodstream infection prediction model, reading the real-time data to be tested of the patient from the memory unit, and inputting the real-time data to be tested into the bloodstream infection prediction model to generate the bloodstream infection risk probability. The real-time data to be tested includes an intensive care unit detecting data and a blood inspection data of the patient. The intensive care unit detecting data and the blood inspection data are detected during a feature window time interval.

Abstract: An acute kidney injury predicting system and a method thereof are proposed. A processor reads the data to be tested, the detection data, the machine learning algorithm and the risk probability comparison table from a main memory. The processor trains the detection data according to the machine learning algorithm to generate an acute kidney injury prediction model, and inputs the data to be tested into the acute kidney injury prediction model to generate an acute kidney injury characteristic risk probability and a data sequence table. The data sequence table lists the data to be tested in sequence according to a proportion of each of the data to be tested in the acute kidney injury characteristics. The processor selects one of the medical treatment data from the risk probability comparison table according to the acute kidney injury characteristic risk probability.

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.

Abstract: An image recognition method and an electronic apparatus configured for image recognition are provided. A training sample set is provided to train a recognition model including neural networks to recognize a classification label to which an image to be tested belongs through the trained recognition model. The training sample set includes image sets respectively belonging to users. During the training process, training images corresponding to classification labels are obtained from a first image set in the training sample set as reference images for training; a training image is obtained from a second image set different from the first image set as an input image for training; the reference images for training and the input image for training are obtained as inputs to the neural networks for training. The input to each neural network includes at least one of the reference images for training and the input image for training.

Abstract: A medical care system for assisting multi-diseases decision-making and real-time information feedback with artificial intelligence technology provided by the invention is capable of obtaining N training models respectively by mathematically operating M different diseases correspondingly based on a batch of collected medical information, obtaining inference results related to at least two diseases by inputting a single patient's data into all or part of the training models to perform mathematical calculation, at the same time, receiving feedback from professionals on the inference results to effectively integrate objective medical data of the patient with subjective medical data of the professionals, and constructing a multi-diseases data model based on the integrated data to be used as a tool for assisting multi-diseases decision-making.