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Applications of Machine Learning in Palliative Care: A Systematic Review

SIMPLE SUMMARY: To investigate the adoption of machine learning in palliative care research and clinical practice, we systematically searched for published research papers on the topic. We found several publications that used different kinds of machine learning in palliative care for different use c...

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Autores principales: Vu, Erwin, Steinmann, Nina, Schröder, Christina, Förster, Robert, Aebersold, Daniel M., Eychmüller, Steffen, Cihoric, Nikola, Hertler, Caroline, Windisch, Paul, Zwahlen, Daniel R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10001037/
https://www.ncbi.nlm.nih.gov/pubmed/36900387
http://dx.doi.org/10.3390/cancers15051596
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author Vu, Erwin
Steinmann, Nina
Schröder, Christina
Förster, Robert
Aebersold, Daniel M.
Eychmüller, Steffen
Cihoric, Nikola
Hertler, Caroline
Windisch, Paul
Zwahlen, Daniel R.
author_facet Vu, Erwin
Steinmann, Nina
Schröder, Christina
Förster, Robert
Aebersold, Daniel M.
Eychmüller, Steffen
Cihoric, Nikola
Hertler, Caroline
Windisch, Paul
Zwahlen, Daniel R.
author_sort Vu, Erwin
collection PubMed
description SIMPLE SUMMARY: To investigate the adoption of machine learning in palliative care research and clinical practice, we systematically searched for published research papers on the topic. We found several publications that used different kinds of machine learning in palliative care for different use cases. However, on average, there needs to be more rigorous testing of the models to ensure that they work well in different settings. ABSTRACT: 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.
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spelling pubmed-100010372023-03-11 Applications of Machine Learning in Palliative Care: A Systematic Review Vu, Erwin Steinmann, Nina Schröder, Christina Förster, Robert Aebersold, Daniel M. Eychmüller, Steffen Cihoric, Nikola Hertler, Caroline Windisch, Paul Zwahlen, Daniel R. Cancers (Basel) Systematic Review SIMPLE SUMMARY: To investigate the adoption of machine learning in palliative care research and clinical practice, we systematically searched for published research papers on the topic. We found several publications that used different kinds of machine learning in palliative care for different use cases. However, on average, there needs to be more rigorous testing of the models to ensure that they work well in different settings. ABSTRACT: 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. MDPI 2023-03-04 /pmc/articles/PMC10001037/ /pubmed/36900387 http://dx.doi.org/10.3390/cancers15051596 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/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://creativecommons.org/licenses/by/4.0/).
spellingShingle Systematic Review
Vu, Erwin
Steinmann, Nina
Schröder, Christina
Förster, Robert
Aebersold, Daniel M.
Eychmüller, Steffen
Cihoric, Nikola
Hertler, Caroline
Windisch, Paul
Zwahlen, Daniel R.
Applications of Machine Learning in Palliative Care: A Systematic Review
title Applications of Machine Learning in Palliative Care: A Systematic Review
title_full Applications of Machine Learning in Palliative Care: A Systematic Review
title_fullStr Applications of Machine Learning in Palliative Care: A Systematic Review
title_full_unstemmed Applications of Machine Learning in Palliative Care: A Systematic Review
title_short Applications of Machine Learning in Palliative Care: A Systematic Review
title_sort applications of machine learning in palliative care: a systematic review
topic Systematic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10001037/
https://www.ncbi.nlm.nih.gov/pubmed/36900387
http://dx.doi.org/10.3390/cancers15051596
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