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https://www.selleckchem.com/products/skf-34288-hydrochloride.html 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 identified from this specific population. Blood urea nitrogen, creatinine, potassium, hemoglobin, and red blood cell were distinctive between the clusters. Cluster Three presented the highest blood products transfusion rate (19.8%), followed by Cluster One (15.5%) and cluster Two (9.3%), which was significantly different. Hemodialysis was more frequently provided to Cluster Three while bronchoscopy was done to Cluster One and Two. Cluster Three showed the highest mortality (30.4%), which was more than two-fold compared to Cluster One (14.1%) and Two (12.2%). Conclusion Three subgroups were identified and their clinical characteristics were compared. These findings may be useful to anticipate treatment strategies and probable outcomes of ICU patients. Unsupervised machine learning may enable ICU multi-dimensional data to be organized and to make sense of the data.The spatio-temporal complexity of groundwater storage change is a result of interconnected impact of socio-ecological factors. Previous research indicates several socio-ecological factors (e.g. human extraction, land cover change, and climate change) that may result in groundwater depletion. However, we seldom have empirical studies that provide spatio-temporally explicit information on the main drivers among these factors that determine regional groundwater change. This research explored a spatio-temporally explicit

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