Stratifying Mortality Risk in Intensive Care: A Comprehensive Analysis Using Cluster Analysis and Classification and Regression Tree Algorithms

Background Machine learning (ML) can be promising for stratifying patients into homogeneous groups and assessing mortality based on score combination. Using ML, we compared mortality prediction performance for clustered and non-clustered models and tried to develop a simple decision algorithm to pre...

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Vydáno v:	Intensive care research Ročník 4; číslo 2; s. 116 - 128
Hlavní autoři:	Romanelli, Antonio, Palmese, Salvatore, De Vita, Serena, Calicchio, Alessandro, Gammaldi, Renato
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Dordrecht Springer Netherlands 01.06.2024 Springer Nature B.V Springer
Témata:	Algorithms Body mass index Charlson Comorbidity Index Classification and regression tree Cluster analysis Data collection Emergency medical care Gender Hospitals Hypotheses Intensive care Medical prognosis Medicine Medicine & Public Health Missing data Mortality Ostomy Patients Research Article Sequential Organ Failure Assessment Simplified Acute Physiology Score II Survival analysis Variables Cluster analysis Classification and regression tree Simplified Acute Physiology Score II Charlson Comorbidity Index Mortality Sequential Organ Failure Assessment
ISSN:	2666-9862, 2666-9862
On-line přístup:	Získat plný text
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