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                <text>Dominio científico: Coronavirus</text>
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              <text>U-survival for prognostic prediction of disease progression and mortality of patients with COVID-19</text>
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              <text>Janne J. Näppi, Tomoki Uemura, Chinatsu Watari, Toru Hironaka, Tohru Kamiya, Hiroyuki Yoshida</text>
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              <text>Abstract The rapid increase of patients with coronavirus disease 2019 (COVID-19) has introduced major challenges to healthcare services worldwide. Therefore, fast and accurate clinical assessment of COVID-19 progression and mortality is vital for the management of COVID-19 patients. We developed an automated image-based survival prediction model, called U-survival, which combines deep learning of chest CT images with the established survival analysis methodology of an elastic-net Cox survival model. In an evaluation of 383 COVID-19 positive patients from two hospitals, the prognostic bootstrap prediction performance of U-survival was significantly higher (P </text>
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              <text>2021</text>
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              <text>10.1038/s41598-021-88591-z</text>
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              <text>Epidemiology and Health</text>
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              <text>Korean Society of Epidemiology</text>
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              <text>Science, Medicine</text>
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