Machine learning models to predict adverse outcomes using glim combinations with and without muscle mass in people with cancer Kiss, Nicole, Steer, B, de van der Schueren, M, Loeliger, J, Alizadehsani, Roohallah, Edbrooke, L, Deftereos, I, Laing, E and Khosravi, Abbas 2021, Machine learning models to predict adverse outcomes using glim combinations with and without muscle mass in people with cancer, Clinical Nutrition ESPEN, vol. 46, pp. S566-S567, doi: 10.1016/j.clnesp.2021.09.076. Attached Files Name Description MIMEType Size Downloads Title Machine learning models to predict adverse outcomes using glim combinations with and without muscle mass in people with cancer Author(s) Kiss, Nicole orcid.org/0000-0002-6476-9834 Steer, B de van der Schueren, M Loeliger, J Alizadehsani, Roohallah Edbrooke, L Deftereos, I Laing, E orcid.org/0000-0001-6927-0744 Khosravi, Abbas Journal name Clinical Nutrition ESPEN Volume number 46 Start page S566 End page S567 Total pages 2 Publisher Elsevier Place of publication Amsterdam, The Netherlands Publication date 2021-12 ISSN 2405-4577 Language eng DOI 10.1016/j.clnesp.2021.09.076 HERDC Research category C1 Refereed article in a scholarly journal Persistent URL http://hdl.handle.net/10536/DRO/DU:30168330 Document type: Journal Article Collections: Faculty of Health School of Exercise and Nutrition Sciences Institute for Physical Activity and Nutrition Related Links Link Description Connect to published version Go to link with your DU access privileges   Connect to Elements publication management system Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Wed, 18 May 2022, 09:29:31 EST Wed, 18 May 2022, 16:27:12 EST Wed, 18 May 2022, 16:36:48 EST Mon, 04 Jul 2022, 06:03:33 EST Wed, 06 Jul 2022, 15:00:20 EST Wed, 06 Jul 2022, 15:27:52 EST Wed, 06 Jul 2022, 15:34:09 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 14 Abstract Views, 0 File Downloads  -  Detailed Statistics Created: Wed, 18 May 2022, 09:29:31 EST