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Background and objective Identification of subgroups may be useful to understand the clinical characteristics of ICU patients. The purposes of this study were to apply an unsupervised machine learning method to ICU patient data to discover subgroups among them; and to examine their clinical characteristics, therapeutic procedures conducted during the ICU stay, and discharge dispositions. Methods K-means clustering method was used with 1503 observations and 9 types of laboratory test results as features. Results Three clusters were ident
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