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

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 val...

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Detalles Bibliográficos
Autores principales: Ferroni, Patrizia, Zanzotto, Fabio M., Scarpato, Noemi, Riondino, Silvia, Guadagni, Fiorella, Roselli, Mario
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2017
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5623790/
https://www.ncbi.nlm.nih.gov/pubmed/29104344
http://dx.doi.org/10.1155/2017/8781379

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5623790/
https://www.ncbi.nlm.nih.gov/pubmed/29104344
http://dx.doi.org/10.1155/2017/8781379

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