Table of Contents Author Guidelines Submit a Manuscript
Disease Markers
Volume 2017, Article ID 8781379, 7 pages
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; ti.eleaffarnas@ingadaug.alleroif

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.


Using kernel machine learning (ML) and random optimization (RO) techniques, we recently developed a set of venous thromboembolism (VTE) risk predictors, which could be useful to devise a web interface for VTE risk stratification in chemotherapy-treated cancer patients. This study was designed to validate a model incorporating the two best predictors and to compare their combined performance with that of the currently recommended Khorana score (KS). Age, sex, tumor site/stage, hematological attributes, blood lipids, glycemic indexes, liver and kidney function, BMI, performance status, and supportive and anticancer drugs of 608 cancer outpatients were all entered in the model, with numerical attributes analyzed as continuous values. VTE rate was 7.1%. The VTE risk prediction performance of the combined model resulted in 2.30 positive likelihood ratio (+LR), 0.46 negative LR (−LR), and 4.88 HR (95% CI: 2.54–9.37), with a significant improvement over the KS [HR 1.73 (95% CI: 0.47–6.37)]. These results confirm that a ML approach might be of clinical value for VTE risk stratification in chemotherapy-treated cancer outpatients and suggest that the ML-RO model proposed could be useful to design a web service able to provide physicians with a graphical interface helping in the critical phase of decision making.