[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117914-en":3,"doc-seo-117914-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117914,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Applications of Machine Learning in Palliative Care - A Systematic Review","Objective: summarize the available literature on using machine learning (ML) for palliative care practice and research, and evaluate how well published studies follow key ML best practices. Methods: MEDLINE was searched for ML use in palliative care practice or research, and records were screened using PRISMA guidelines. Results: 22 publications were included, mostly using supervised or unsupervised models such as tree-based classifiers and neural networks, mainly for mortality prediction. Conclusions: ML in palliative care is used mainly for mortality prediction, while external test sets and prospective validations remain rare.","Zurich Open Repository and Archive  \nUniversity of Zurich  \nUniversity Library Strickhofstrasse 39  \nCH-8057 Zurich [www.zora.uzh.ch](www.zora.uzh.ch)  \nYear: 2023  \nApplications of Machine Learning in Palliative Care: A Systematic Review  \nVu, Erwin ; Steinmann, Nina ; Schröder, Christina ; Förster, Robert ; Aebersold, Daniel M ; Eychmüller, Steffen ; Cihoric, Nikola ; Hertler, Caroline ; Windisch, Paul ; Zwahlen, Daniel R  \nAbstract: Objective: To summarize the available literature on using machine learning (ML) for palliative care practice as well as research and to assess the adherence of the published studies to the most important ML best practices. Methods: The MEDLINE database was searched for the use of ML in palliative care practice or research, and the records were screened according to PRISMA guidelines. Results: In total, 22 publications using machine learning for mortality prediction (n = 15), data annotation (n = 5), predicting morbidity under palliative therapy (n = 1), and predicting response to palliative therapy (n = 1) were included. Publications used a variety of supervised or unsupervised models, but mostly tree-based classifiers and neural networks. Two publications had code uploaded to a public repository, and one publication uploaded the dataset. Conclusions: Machine learning in palliative care is mainly used to predict mortality. Similarly to other applications of ML, external test sets and prospective validations are the exception.  \nDOI: [https://doi.org/10.3390/cancers15051596](https://doi.org/10.3390/cancers15051596)  \nPosted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: [https://doi.org/10.5167/uzh-234772](https://doi.org/10.5167/uzh-234772)  \nJournal Article Published Version  \nThe following work is licensed under a Creative Commons: Attribution 4.0 International (CC BY 4.0) License.  \nOriginally published at:  \nVu, Erwin; Steinmann, Nina; Schröder, Christina; Förster, Robert; Aebersold, Daniel M; Eychmüller, Steffen; Cihoric, Nikola; Hertler, Caroline; Windisch, Paul; Zwahlen, Daniel R (2023) . Applications of Machine Learning in Palliative Care: A Systematic Review. Cancers, 15(5):1596 .  \nDOI: [https://doi.org/10.3390/cancers15051596](https://doi.org/10.3390/cancers15051596)  \n cancers   \nSystematic Review  \nApplications of Machine Learning in Palliative Care: A Systematic Review  \nErwin Vu 1,†, Nina Steinmann 2,†, Christina Schröder 2, Robert Förster 2, Daniel M. Aebersold 3,  \nSteffen Eychmüller 3,4, Nikola Cihoric 3, Caroline Hertler 5, Paul Windisch 2, *,† and Daniel R. Zwahlen 2,†  \nCitation: Vu, E.; Steinmann, N.; Schröder, C.; Förster, R.; Aebersold, D.M.; Eychmüller, S.; Cihoric, N.; Hertler, C.; Windisch, P.; Zwahlen, D.R. Applications of Machine Learning in Palliative Care: A Systematic Review. Cancers 2023, 15, 1596. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)cancers15051596  \nAcademic Editors: Philipp Lenz and Mitra Tewes  \nReceived: 7 February 2023  \nRevised: 24 February 2023  \nAccepted: 2 March 2023  \nPublished: 4 March 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Radiation Oncology, Kantonsspital St. Gallen, 9007 St. Gallen, Switzerland  \n2 Department of Radiation Oncology, Kantonsspital Winterthur, Brauerstrasse 15, Haus R, 8400 Winterthur, Switzerland  \n3 Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern, 3012 Bern, Switzerland  \n4 University Center for Palliative Care, Inselspital, Bern University Hospital, University of Bern, 3012 Bern, Switzerland  \n5 Competence Center for Palliative Care, Department of Radiation Oncology, University Ho","cbCaimPvEnKIlTqV","https://ap.wps.com/l/cbCaimPvEnKIlTqV","pdf",425761,1,13,"English","en",105,"# Introduction\n# Methods\n## Search strategy and screening\n# Results\n## Included studies and model types\n## Use cases and datasets/code availability\n# Conclusions\n# Keywords","[{\"question\":\"What is the primary goal of this systematic review?\",\"answer\":\"To summarize literature on ML use in palliative care practice and research and to assess adherence to important ML best practices.\"},{\"question\":\"How were studies identified and selected?\",\"answer\":\"MEDLINE was searched for ML in palliative care practice or research, and records were screened following PRISMA guidelines.\"},{\"question\":\"What did the review find about how machine learning is mainly used in palliative care?\",\"answer\":\"Most included publications applied ML to mortality prediction, while external test sets and prospective validations were uncommon.\"}]","Applications of Machine Learning in Palliative Care - 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