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.

Ferroni, P., Zanzotto, F.m., Scarpato, N., Riondino, S., Guadagni, F., & Roselli, M. (2017). Validation of a machine learning approach for venous thromboembolism risk prediction in oncology. DISEASE MARKERS, 2017, 8781379 [10.1155/2017/8781379].

Validation of a machine learning approach for venous thromboembolism risk prediction in oncology

Zanzotto, Fabio M.;Scarpato, Noemi;Riondino, Silvia;Roselli, Mario
2017

Abstract

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.
Pubblicato
Rilevanza internazionale
Articolo
Esperti anonimi
Settore INF/01 - Informatica
Settore ING-INF/05 - Sistemi di Elaborazione delle Informazioni
Settore MED/06 - Oncologia Medica
English
Molecular Biology; Genetics; Clinical Biochemistry; Biochemistry (medical)
http://www.hindawi.com/journals/dm/contents/
Ferroni, P., Zanzotto, F.m., Scarpato, N., Riondino, S., Guadagni, F., & Roselli, M. (2017). Validation of a machine learning approach for venous thromboembolism risk prediction in oncology. DISEASE MARKERS, 2017, 8781379 [10.1155/2017/8781379].
Ferroni, P; Zanzotto, Fm; Scarpato, N; Riondino, S; Guadagni, F; Roselli, M
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2108/190401
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