Validation of a Machine Learning Approach for Venous Thromboembolism Risk Prediction in Oncology
Joint Authors
Ferroni, Patrizia
Guadagni, Fiorella
Roselli, Mario
Zanzotto, Fabio M.
Scarpato, Noemi
Riondino, Silvia
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-09-17
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Ferroni, Patrizia& Zanzotto, Fabio M.& Scarpato, Noemi& Riondino, Silvia& Guadagni, Fiorella& Roselli, Mario. 2017. Validation of a Machine Learning Approach for Venous Thromboembolism Risk Prediction in Oncology. Disease Markers،Vol. 2017, no. 2017, pp.1-7.
https://search.emarefa.net/detail/BIM-1152565
Modern Language Association (MLA)
Ferroni, Patrizia…[et al.]. Validation of a Machine Learning Approach for Venous Thromboembolism Risk Prediction in Oncology. Disease Markers No. 2017 (2017), pp.1-7.
https://search.emarefa.net/detail/BIM-1152565
American Medical Association (AMA)
Ferroni, Patrizia& Zanzotto, Fabio M.& Scarpato, Noemi& Riondino, Silvia& Guadagni, Fiorella& Roselli, Mario. Validation of a Machine Learning Approach for Venous Thromboembolism Risk Prediction in Oncology. Disease Markers. 2017. Vol. 2017, no. 2017, pp.1-7.
https://search.emarefa.net/detail/BIM-1152565
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1152565