{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,10,24]],"date-time":"2023-10-24T05:20:50Z","timestamp":1698124850421},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643684482","type":"print"},{"value":"9781643684499","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,10,19]],"date-time":"2023-10-19T00:00:00Z","timestamp":1697673600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,10,19]]},"abstract":"<jats:p>This article focuses on the use of deep learning synthetic data generation methods to assess the risk of future treatments and medication for preventing venous thromboembolism (VTE) in cancer patients, based on a small dataset of genetic and clinical variables. The study employs CopulaGANs to generate synthetic tabular data, which is then used to train a Deep Learning-based classifier using domain adaptation techniques. The trained model is fine-tuned using real data and performs better than current state-of-the-art medical scores in assessing VTE risk. Additionally, the resulting Precision-Recall curve offers flexibility in selecting different and better operational points for VTE risk assessment.<\/jats:p>","DOI":"10.3233\/faia230681","type":"book-chapter","created":{"date-parts":[[2023,10,23]],"date-time":"2023-10-23T08:14:45Z","timestamp":1698048885000},"source":"Crossref","is-referenced-by-count":0,"title":["Assessing VTE Risk in Cancer Patients Using Deep Learning Synthetic Data Generation and Domain Adaptation Techniques"],"prefix":"10.3233","author":[{"given":"Sergi","family":"Bech","sequence":"first","affiliation":[{"name":"Dept. Matem\u00e0tiques i Inform\u00e0tica, Universitat de Barcelona, Barcelona, Spain"}]},{"given":"B\u00e1rbara","family":"Lobato","sequence":"additional","affiliation":[{"name":"Unitat de Gen\u00f2mica de Malalties Complexes, Institut de Recerca de l\u2019Hospital de la Santa Creu i Sant Pau, Barcelona, Spain"}]},{"given":"Oriol","family":"Pujol","sequence":"additional","affiliation":[{"name":"Dept. Matem\u00e0tiques i Inform\u00e0tica, Universitat de Barcelona, Barcelona, Spain"}]},{"given":"Jos\u00e9 Manuel","family":"Soria","sequence":"additional","affiliation":[{"name":"Unitat de Gen\u00f2mica de Malalties Complexes, Institut de Recerca de l\u2019Hospital de la Santa Creu i Sant Pau, Barcelona, Spain"}]},{"given":"Andr\u00e9s","family":"Mu\u00f1oz","sequence":"additional","affiliation":[{"name":"Medical Oncology, Hospital General Universitario Gregorio Mara\u00f1\u00f3n; and Cancer and Thrombosis Working Section, Spanish Society of Medical Oncology (SEOM), Madrid, Spain"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Artificial Intelligence Research and Development"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA230681","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,23]],"date-time":"2023-10-23T08:14:45Z","timestamp":1698048885000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA230681"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,19]]},"ISBN":["9781643684482","9781643684499"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia230681","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,19]]}}}