Author: Zhao, Chun-Hong; Wu, Hui-Tao; Che, He-Bin; Song, Ya-Nan; Zhao, Yu-Zhuo; Li, Kai-Yuan; Xiao, Hong-Ju; Zhai, Yong-Zhi; Liu, Xin; Lu, Hong-Xi; Li, Tan-Shi
Title: Prediction of fatal adverse prognosis in patients with fever-related diseases based on machine learning: A retrospective study Document date: 2020_3_5
ID: tk3861u0_18
Snippet: Modern medicine often involves collecting large amounts of physiological data, laboratory results and imaging data into electronic records. The data, however, are complex and multidimensional. It is difficult to find subtle relationships between these data and clinical outcomes using traditional statistical techniques. In this study, the advantage of machine learning is to provide possible innovative solutions for clinical doubts, to find importa.....
Document: Modern medicine often involves collecting large amounts of physiological data, laboratory results and imaging data into electronic records. The data, however, are complex and multidimensional. It is difficult to find subtle relationships between these data and clinical outcomes using traditional statistical techniques. In this study, the advantage of machine learning is to provide possible innovative solutions for clinical doubts, to find important indicators that may be ignored in treatment for clinicians, and to provide guidance for clinical decision-making to prevent adverse prognosis.
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