Tunghai University

Taichung Veterans General Hospital

Kai-Chih Pai

Chien-Lun Liao

Lun-Chi Chen

Ta-Chun Hung

Chieh-Liang Wu

Min-Shian Wang

Yan-Nan Lin

Ruey-Kai Sheu

Chien-Chung Huang

Yu-Jen Huang

Cheng-Hsu Chen

Chun-Te Huang

Chun-Ming Lai

Ming-Cheng Chan

Tsai-Jung Wang

Shang-Feng Tsai

Wen-Cheng Chao

Chia-Tien Hsu

Hui-Chiao Yang

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.

Acute kidney injury predicting system and method thereof

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.

Respiratory status classifying method and system thereof

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Taichung, Taiwan

Kai-Chih Pai