{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T05:28:15Z","timestamp":1740461295882,"version":"3.37.3"},"reference-count":0,"publisher":"IOS Press","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2009]]},"abstract":"<jats:p>Acute Myeloid Leukaemia (AML) is a serious condition that may require aggressive systemic treatment. As a consequence of this it is important to characterize quantitatively response to treatment, differentiating patients across a range of clinical and laboratory indicators. This study follows the disease progression for a cohort of n=509 patients diagnosed with AML &amp;ldquo;de novo&amp;rdquo; and treated according to a strict protocol defined by the &amp;ldquo;Gruppo Italiano Malattie Ematologiche dell'Adulto&amp;rdquo; (GIMEMA). This protocol involves an induction therapy with health assessment typically within 60&amp;ndash;90 days and three possible outcomes: complete remission (CR), resistance to induction therapy (Res) and induction death (ID). Accordingly, a time-to-event model with competing risks using the framework of partial logistic artificial neural networks with automatic relevance determination (PLANNCR-ARD) is applied. This results show a stratification of the mortality risk following therapy.<\/jats:p>","DOI":"10.3233\/978-1-60750-010-0-81","type":"book-chapter","created":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T11:59:50Z","timestamp":1740398390000},"source":"Crossref","is-referenced-by-count":0,"title":["Short-term time-to-event model of response to treatment following the GIMEMA protocol for Acute Myeloid Leukaemia"],"prefix":"10.3233","author":[{"family":"Lisboa Paulo J.G.","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Jarman Ian H.","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Etchells Terence A.","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Ambrogi Federico","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Ardoino Ilaria","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Vignetti Marco","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Biganzoli Elia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Computational Intelligence and Bioengineering"],"original-title":[],"deposited":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T12:28:00Z","timestamp":1740400080000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.medra.org\/servlet\/aliasResolver?alias=iospressISSNISBN&issn=0922-6389&volume=196&spage=81"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2009]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/978-1-60750-010-0-81","relation":{},"ISSN":["0922-6389"],"issn-type":[{"value":"0922-6389","type":"print"}],"subject":[],"published":{"date-parts":[[2009]]}}}