{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,5,25]],"date-time":"2022-05-25T12:43:02Z","timestamp":1653482582321},"reference-count":0,"publisher":"IOS Press","license":[{"start":{"date-parts":[[2022,5,25]],"date-time":"2022-05-25T00:00:00Z","timestamp":1653436800000},"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":[[2022,5,25]]},"abstract":"<jats:p>In many countries, the management of cancer patients must be discussed in multidisciplinary tumor boards (MTBs). These meetings have been introduced to provide a collaborative and multidisciplinary approach to cancer care. However, the benefits of MTBs are now being challenged because there are a lot of cases and not enough time to discuss all the of them. During the evaluation of the guideline-based clinical decision support system (CDSS) of the DESIREE project, we found that for some clinical cases, the system did not produce recommendations. We assumed that these cases were complex clinical cases and needed deeper MTB discussions. In this work, we trained and tested several machine learning and deep learning algorithms on a labelled sample of 298 breast cancer patient summaries, to predict the complexity of a breast cancer clinical case. XGboost and multi-layer perceptron were the models with the best result, with an F1 score of 83%.<\/jats:p>","DOI":"10.3233\/shti220400","type":"book-chapter","created":{"date-parts":[[2022,5,25]],"date-time":"2022-05-25T12:12:44Z","timestamp":1653480764000},"source":"Crossref","is-referenced-by-count":0,"title":["Using Machine Learning and Deep Learning Methods to Predict the Complexity of Breast Cancer Cases"],"prefix":"10.3233","author":[{"given":"Akram","family":"Redjdal","sequence":"first","affiliation":[{"name":"Sorbonne Universit\u00e9, Universit\u00e9 Sorbonne Paris Nord, INSERM, Laboratoire d\u2019Informatique M\u00e9dicale et d\u2019Ing\u00e9nierie des connaissances en e-Sant\u00e9, LIMICS, F-75006 Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jacques","family":"Bouaud","sequence":"additional","affiliation":[{"name":"Sorbonne Universit\u00e9, Universit\u00e9 Sorbonne Paris Nord, INSERM, Laboratoire d\u2019Informatique M\u00e9dicale et d\u2019Ing\u00e9nierie des connaissances en e-Sant\u00e9, LIMICS, F-75006 Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joseph","family":"Gligorov","sequence":"additional","affiliation":[{"name":"Sorbonne Universit\u00e9, Institut Universitaire de Canc\u00e9rologie, Paris, France"},{"name":"AP-HP, H\u00f4pital Tenon, Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Brigitte","family":"Seroussi","sequence":"additional","affiliation":[{"name":"Sorbonne Universit\u00e9, Universit\u00e9 Sorbonne Paris Nord, INSERM, Laboratoire d\u2019Informatique M\u00e9dicale et d\u2019Ing\u00e9nierie des connaissances en e-Sant\u00e9, LIMICS, F-75006 Paris, France"},{"name":"AP-HP, H\u00f4pital Tenon, Paris, France"},{"name":"APREC, Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Challenges of Trustable AI and Added-Value on Health"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI220400","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,25]],"date-time":"2022-05-25T12:12:44Z","timestamp":1653480764000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI220400"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,25]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti220400","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,25]]}}}