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_2
Snippet: Because of the complexity of fever-related illnesses, no biomarker can definitely diagnose sepsis or predict its clinical outcome. [7] General-purpose illness severity scoring systems such as the Acute Physiology and Chronic Health Evaluation II often contain too many complex items or are not specific to people with fever. [8, 9] With the continuous development of machine learning technology, [10, 11] a machine learning approach has outperformed .....
Document: Because of the complexity of fever-related illnesses, no biomarker can definitely diagnose sepsis or predict its clinical outcome. [7] General-purpose illness severity scoring systems such as the Acute Physiology and Chronic Health Evaluation II often contain too many complex items or are not specific to people with fever. [8, 9] With the continuous development of machine learning technology, [10, 11] a machine learning approach has outperformed existing clinical decision rules as well as traditional analytic techniques for predicting in-hospital mortality of emergency department (ED) patients with sepsis. [12] This study, using big data analysis technology, aimed to explore the key factors associated with adverse prognosis of patients with febrile illness, establish an effective model to predict fatal adverse prognosis in patients with febrile disease, and provide technical support for auxiliary clinical diagnosis and treatment decision-making.
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