Improved cardiovascular risk prediction in patients with end-stage renal disease on hemodialysis using machine learning modeling and circulating microribonucleic acids

De Gonzalo-Calvo D, Martinez-Camblor P, Baer C, Duarte K, Girerd N, Fellstrom B, Schmieder R, Jardine AG, Massy ZA, Holdaas H, Rossignol P, Zannad F, Thum T (2020)

Publication Type: Journal article

Publication year: 2020

Journal

Theranostics Ivyspring International Publisher

Book Volume: 10

Pages Range: 8665-8676

Journal Issue: 19

DOI: 10.7150/thno.46123

Abstract

Rationale: To test whether novel biomarkers, such as microribonucleic acids (miRNAs), and nonstandard predictive models, such as decision tree learning, provide useful information for medical decision-making in patients on hemodialysis (HD).

Authors with CRIS profile

Roland Schmieder Professur für Innere Medizin (Nephrologie)

Involved external institutions

Medizinische Hochschule Hannover (MHH) / Hannover Medical School DE Germany (DE) Dartmouth College US United States (USA) (US) Université de Lorraine FR France (FR) Uppsala University Hospital / Akademiska sjukhuset SE Sweden (SE) University of Glasgow GB United Kingdom (GB) Assistance Publique-Hôpitaux de Paris (AP-HP) FR France (FR) Oslo University Hospital / Oslo Universitetssykehus Rikshospitalet NO Norway (NO)

How to cite

APA:

De Gonzalo-Calvo, D., Martinez-Camblor, P., Baer, C., Duarte, K., Girerd, N., Fellstrom, B.,... Thum, T. (2020). Improved cardiovascular risk prediction in patients with end-stage renal disease on hemodialysis using machine learning modeling and circulating microribonucleic acids. Theranostics, 10(19), 8665-8676. https://doi.org/10.7150/thno.46123

MLA:

De Gonzalo-Calvo, David, et al. "Improved cardiovascular risk prediction in patients with end-stage renal disease on hemodialysis using machine learning modeling and circulating microribonucleic acids." Theranostics 10.19 (2020): 8665-8676.

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