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Disease Markers
Volume 2017 (2017), Article ID 8781379, 7 pages
https://doi.org/10.1155/2017/8781379
Research Article

Validation of a Machine Learning Approach for Venous Thromboembolism Risk Prediction in Oncology

1Department of Human Sciences and Quality of Life Promotion, San Raffaele Roma Open University, 00166 Rome, Italy
2Interinstitutional Multidisciplinary Biobank (BioBIM), IRCCS San Raffaele Pisana, 00166 Rome, Italy
3Department of Enterprise Engineering, University of Rome “Tor Vergata”, 00133 Rome, Italy
4Department of Systems Medicine, Medical Oncology, University of Rome “Tor Vergata”, 00133 Rome, Italy

Correspondence should be addressed to Fiorella Guadagni

Received 13 June 2017; Accepted 30 July 2017; Published 17 September 2017

Academic Editor: Dennis W. T. Nilsen

Copyright © 2017 Patrizia Ferroni et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to Cite this Article

Patrizia Ferroni, Fabio M. Zanzotto, Noemi Scarpato, Silvia Riondino, Fiorella Guadagni, and Mario Roselli, “Validation of a Machine Learning Approach for Venous Thromboembolism Risk Prediction in Oncology,” Disease Markers, vol. 2017, Article ID 8781379, 7 pages, 2017. doi:10.1155/2017/8781379