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Classifying individuals based on a densely captured sequence of vital signs: An example using repeated blood pressure measurements during hemodialysis treatment.

, , and . J. Biomed. Informatics, (2015)

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Classifying individuals based on a densely captured sequence of vital signs: An example using repeated blood pressure measurements during hemodialysis treatment., , and . J. Biomed. Informatics, (2015)Changes during dialysis captured in electronic health records help predict near-term risk of sudden cardiac death., , and . AMIA, AMIA, (2013)Evolving statistical methods to facilitate evaluation of the causal association between erythropoiesis-stimulating agent dose and mortality in nonexperimental research: strengths and limitations., , , , , , , , , and . American journal of kidney diseases : the official journal of the National Kidney Foundation, 54 (3): 554-60 (September 2009)5279<m:linebreak></m:linebreak>JID: 8110075; 0 (Hematinics); RF: 40; 2008/12/19 received; 2009/05/06 accepted; 2009/07/09 aheadofprint; ppublish;.Propensity scores: help or hype?, and . Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association, 19 (7): 1671-3 (July 2004)3603<br/>Propensity score.Predicting mortality over different time horizons: which data elements are needed?, , , and . J. Am. Medical Informatics Assoc., 24 (1): 176-181 (2017)