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Prediction of sepsis within 24 hours at the triage stage in emergency departments using machine learning
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作者 Jingyuan Xie Jiandong Gao +8 位作者 Mutian Yang Ting Zhang yecheng liu Yutong Chen Zetong liu Qimin Mei Zhimao Li Huadong Zhu Ji Wu 《World Journal of Emergency Medicine》 SCIE CAS CSCD 2024年第5期379-385,共7页
BACKGROUND:Sepsis is one of the main causes of mortality in intensive care units(ICUs).Early prediction is critical for reducing injury.As approximately 36%of sepsis occur within 24 h after emergency department(ED)adm... BACKGROUND:Sepsis is one of the main causes of mortality in intensive care units(ICUs).Early prediction is critical for reducing injury.As approximately 36%of sepsis occur within 24 h after emergency department(ED)admission in Medical Information Mart for Intensive Care(MIMIC-IV),a prediction system for the ED triage stage would be helpful.Previous methods such as the quick Sequential Organ Failure Assessment(qSOFA)are more suitable for screening than for prediction in the ED,and we aimed to fi nd a light-weight,convenient prediction method through machine learning.METHODS:We accessed the MIMIC-IV for sepsis patient data in the EDs.Our dataset comprised demographic information,vital signs,and synthetic features.Extreme Gradient Boosting(XGBoost)was used to predict the risk of developing sepsis within 24 h after ED admission.Additionally,SHapley Additive exPlanations(SHAP)was employed to provide a comprehensive interpretation of the model's results.Ten percent of the patients were randomly selected as the testing set,while the remaining patients were used for training with 10-fold cross-validation.RESULTS:For 10-fold cross-validation on 14,957 samples,we reached an accuracy of 84.1%±0.3%and an area under the receiver operating characteristic(ROC)curve of 0.92±0.02.The model achieved similar performance on the testing set of 1,662 patients.SHAP values showed that the fi ve most important features were acuity,arrival transportation,age,shock index,and respiratory rate.CONCLUSION:Machine learning models such as XGBoost may be used for sepsis prediction using only a small amount of data conveniently collected in the ED triage stage.This may help reduce workload in the ED and warn medical workers against the risk of sepsis in advance. 展开更多
关键词 SEPSIS Machine learning Emergency department TRIAGE Informatics
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Factors associated with dysglycemia and its post-surgical resolution in patients with pheochromocytomas
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作者 Lin Zhao Xu Meng +10 位作者 Qiongyu Zhang Yi Zhou Ting Zhang Tao Tian Hua Fan Huadong Zhu yecheng liu Fang Luo Linping Wang Weixian Yang Xianliang Zhou 《Science China(Life Sciences)》 SCIE CAS CSCD 2023年第2期411-414,共4页
Dear Editor,Pheochromocytomas are rare neuroendocrine tumors originating from chromaffin cells in the adrenal medulla(Neumann et al., 2019). A number of case reports and small cohorts have reported the association bet... Dear Editor,Pheochromocytomas are rare neuroendocrine tumors originating from chromaffin cells in the adrenal medulla(Neumann et al., 2019). A number of case reports and small cohorts have reported the association between dysglycemia and pheochromocytomas(Elenkova et al., 2020;Khatiwada et al., 2020;Krumeich et al., 2021). However, there are few large sample studies reporting the outcomes of dysglycemia in patients with pheochromocytomas after resection. 展开更多
关键词 PATIENTS cytoma
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Synthesis and Biological Evaluation of Gem-Difluoro- methylenated Statin Derivatives as Highly Potent HMG-CoA Reductase Inhibitors
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作者 Zhao Zhao Jiaxin Cui +7 位作者 Yan Yin Heng Zhang yecheng liu Rui Zeng Chao Fang Zhenpeng Kai ZhonghuaWang Fanhong Wu 《Chinese Journal of Chemistry》 SCIE CAS CSCD 2016年第8期801-808,共8页
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