Predicting hypoglycemia in critically Ill patients using machine learning and electronic health records-Reference-Cited by-同舟云学术

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Predicting hypoglycemia in critically Ill patients using machine learning and electronic health records

Published:2021-10-04 Issue: Volume: Page:
ISSN:1387-1307
Container-title:Journal of Clinical Monitoring and Computing
language:en
Short-container-title:J Clin Monit Comput

Author:

Mantena Sreekar,Arévalo Aldo Robles,Maley Jason H.,da Silva Vieira Susana M.,Mateo-Collado Roselyn,da Costa Sousa João M.,Celi Leo Anthony

Funder

Fundação da Ciência e da Tecnologia (FCT) PhD Fellowship

National Institute of Biomedical Imaging and Bioengineering

Instituto Superior Técnico Project Grant

European Regional Development Fund

Programa Operacional Regional de Lisboa

Publisher

Springer Science and Business Media LLC

Subject

Anesthesiology and Pain Medicine,Critical Care and Intensive Care Medicine,Health Informatics

Link

https://link.springer.com/content/pdf/10.1007/s10877-021-00760-7.pdf

Reference27 articles.

1. The NICE-SUGAR Study Investigators. Hypoglycemia and Risk of Death in Critically Ill Patients. New Engl J Med. 2012; 367:1108–18.

2. Hulkower RD, Pollack RM, Zonszein J. Understanding hypoglycemia in hospitalized patients. Diabetes Manag. 2014;4:165–76.

3. Brutsaert E, Carey M, Zonszein J. The clinical impact of inpatient hypoglycemia. J Diabetes Complications. 2014;28:565–72.

4. Krinsley JS, Grover A. Severe hypoglycemia in critically ill patients: Risk factors and outcomes. Crit Care Med. 2007;35:2262–7.

5. Cook CB, Kongable GL, Potter DJ, Abad VJ, Leija DE, Anderson M. Inpatient glucose control: a glycemic survey of 126 U.S. hospitals. J Hosp Med. 2009;4:E7–14.

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3. A prediction model for assessing hypoglycemia risk in critically ill patients with sepsis;Heart & Lung;2023-11

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