Abstract
Evaluating disease progression risk is a key issue in medicine that has been revolutionized by the advent of machine learning approaches and the wide availability of medical data in electronic form. It is time to provide physicians with near-to-the-clinical-practice and effective tools to spread this important technological innovation. In this paper, we describe RISK, a web service that implements a multiple kernel learning approach for predicting breast cancer disease progression. We report on the experience of the BIBIOFAR project where RISK Web Predictor has been developed and tested. Results of our system demonstrate that this kind of approaches can effectively support physicians in the evaluation of risk.
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Acknowledgements
This work was partially supported by the European Social Fund, under the Italian Ministry of Education, University and Research PON03PE_00146_1/10 BIBIOFAR (CUP B88F12000730005) to Guadagni F.
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Guadagni, F. et al. (2017). RISK: A Random Optimization Interactive System Based on Kernel Learning for Predicting Breast Cancer Disease Progression. In: Rojas, I., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2017. Lecture Notes in Computer Science(), vol 10208. Springer, Cham. https://doi.org/10.1007/978-3-319-56148-6_16
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DOI: https://doi.org/10.1007/978-3-319-56148-6_16
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