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            <idno type="halRefHtml">&lt;i&gt;12th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI)&lt;/i&gt;, Sep 2016, Thessaloniki, Greece. pp.14-26, &lt;a target="_blank" href="https://dx.doi.org/10.1007/978-3-319-44944-9_2"&gt;&amp;#x27E8;10.1007/978-3-319-44944-9_2&amp;#x27E9;&lt;/a&gt;</idno>
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              <p>In this work, a tool for the survivability prediction of patients with colon or rectal cancer, up to five years after diagnosis and treatment, is presented. Indeed, an accurate survivability prediction is a difficult task for health care professionals and of high concern to patients, so that they can make the most of the rest of their lives. The distinguishing features of the tool include a balance between the number of necessary inputs and prediction performance, being mobile-friendly, and featuring an online learning component that enables the automatic evolution of the prediction models upon the addition of new cases.</p>
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