Machine-learning to stratify diabetic patients using novel cardiac biomarkers and integrative genomics-Reference-Cited by-同舟云学术

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Machine-learning to stratify diabetic patients using novel cardiac biomarkers and integrative genomics

Published:2019-06-11 Issue:1 Volume:18 Page:
ISSN:1475-2840
Container-title:Cardiovascular Diabetology
language:en
Short-container-title:Cardiovasc Diabetol

Author:

Hathaway Quincy A.,Roth Skyler M.,Pinti Mark V.,Sprando Daniel C.,Kunovac Amina,Durr Andrya J.,Cook Chris C.,Fink Garrett K.,Cheuvront Tristen B.,Grossman Jasmine H.,Aljahli Ghadah A.,Taylor Andrew D.,Giromini Andrew P.,Allen Jessica L.,Hollander John M.^ORCID

Funder

National Institutes of Health

National Heart, Lung, and Blood Institute

American Heart Association

Community Foundation for the Ohio Valley

Publisher

Springer Science and Business Media LLC

Subject

Cardiology and Cardiovascular Medicine,Endocrinology, Diabetes and Metabolism

Link

http://link.springer.com/content/pdf/10.1186/s12933-019-0879-0.pdf

Reference62 articles.

1. Bertoluci MC, Rocha VZ. Cardiovascular risk assessment in patients with diabetes. Diabetol Metab Syndr. 2017;9:25.

2. Gregg EW, Cheng YJ, Srinivasan M, Lin J, Geiss LS, Albright AL, Imperatore G. Trends in cause-specific mortality among adults with and without diagnosed diabetes in the USA: an epidemiological analysis of linked national survey and vital statistics data. Lancet. 2018;391(10138):2430–40.

3. Scirica BM. Use of biomarkers in predicting the onset, monitoring the progression, and risk stratification for patients with type 2 diabetes mellitus. Clin Chem. 2017;63(1):186–95.

4. Bonora E, Tuomilehto J. The pros and cons of diagnosing diabetes with A1C. Diabetes Care. 2011;34(Suppl 2):S184–90.

5. Capobianco E. Systems and precision medicine approaches to diabetes heterogeneity: a Big Data perspective. Clin Transl Med. 2017;6(1):23.

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