2019
DOI: 10.1016/j.jns.2019.10.1719
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Sepsis prediction using machine-learning methods: prolonged disorders of consciousness patients

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“…A discrete conditional survival model (DC-S) was introduced in the work of Marshall [ 25 ] with the Classification Tree, Logistic Regression, and Naïve Bayes classification components to predict the length of stay of the babies and sepsis using the baby characteristics known on the first day of admission. KNN, Random Forest, Logistic Regression, Decision Tree, Gradient Boosting, and Naïve Bayes machine learning methods were used by Metskera et al [ 30 ] to predict sepsis in patients hospitalized in the ICU for at least one month. The results showed that the severity of sympathicotonia, XII blood coagulation factor, total protein increase in LH, prolactin, increased natriuretic peptide, a decrease of albumin, cortisol, an increase of fibrinogen, the index of the APTT are the main factors that affect the risk of sepsis.…”
Section: Introductionmentioning
confidence: 99%
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“…A discrete conditional survival model (DC-S) was introduced in the work of Marshall [ 25 ] with the Classification Tree, Logistic Regression, and Naïve Bayes classification components to predict the length of stay of the babies and sepsis using the baby characteristics known on the first day of admission. KNN, Random Forest, Logistic Regression, Decision Tree, Gradient Boosting, and Naïve Bayes machine learning methods were used by Metskera et al [ 30 ] to predict sepsis in patients hospitalized in the ICU for at least one month. The results showed that the severity of sympathicotonia, XII blood coagulation factor, total protein increase in LH, prolactin, increased natriuretic peptide, a decrease of albumin, cortisol, an increase of fibrinogen, the index of the APTT are the main factors that affect the risk of sepsis.…”
Section: Introductionmentioning
confidence: 99%
“…As presented above in the work of Doggart et al [ 22 ], Metskera et al [ 30 ], and Li et al [ 31 ] many types of research have been performed to predict mortality risk in a patient with sepsis in critical places like the ICU by utilizing different features, such as blood factors, heart rate, cortisol, APTT, etc. Besides, Fisal et al.…”
Section: Introductionmentioning
confidence: 99%