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_26
Snippet: In conclusion, big data analysis method was adopted to establish a scientific and objective prediction and evaluation model for adverse prognosis of patients with fever in this study. We found the main clinical indicators of concern, and the prediction model established had high diagnostic accuracy and reliability, which may be conducive to the early identification of critical patients with fever by physicians, thus improving the prognosis of pat.....
Document: In conclusion, big data analysis method was adopted to establish a scientific and objective prediction and evaluation model for adverse prognosis of patients with fever in this study. We found the main clinical indicators of concern, and the prediction model established had high diagnostic accuracy and reliability, which may be conducive to the early identification of critical patients with fever by physicians, thus improving the prognosis of patients with fever. The application of big data analysis combined with medical research is helpful to improve the diagnosis and treatment level of febrile critical diseases and the prevention and control of infectious diseases. Research on adverse event prediction model for critical patients with fever quantifies the recognition of critical diseases related to fever and provides a reference model for other similar clinical decision support studies.
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