Machine Learning Consensus Clustering of Hospitalized Patients with Admission Hyponatremia

Background: The objective of this study was to characterize patients with hyponatremia at hospital admission into clusters using an unsupervised machine learning approach, and to evaluate the short- and long-term mortality risk among these distinct clusters. Methods: We performed consensus cluster a...

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Bibliographic Details
Published in:	Diseases Vol. 9; no. 3; p. 54
Main Authors:	Thongprayoon, Charat, Hansrivijit, Panupong, Mao, Michael A., Vaitla, Pradeep K., Kattah, Andrea G., Pattharanitima, Pattharawin, Vallabhajosyula, Saraschandra, Nissaisorakarn, Voravech, Petnak, Tananchai, Keddis, Mira T., Erickson, Stephen B., Dillon, John J., Garovic, Vesna D., Cheungpasitporn, Wisit
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.08.2021 MDPI
Subjects:	Algorithms Artificial intelligence Bicarbonates Cardiovascular disease Chronic obstructive pulmonary disease Cluster analysis Clustering Congestive heart failure Coronary artery Coronary artery disease Diabetes Diabetes mellitus End-stage renal disease Hemoglobin Hospitalization Hyponatremia Kidney diseases Laboratories Learning algorithms Lung diseases Machine learning Mortality Obstructive lung disease Patients Phenotypes Potassium Respiratory diseases Sodium Urine Variance analysis United States > US
ISSN:	2079-9721, 2079-9721
Online Access:	Get full text
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