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Machine learning with PROs in breast cancer surgery; caution: Collecting PROs at baseline is crucial
As high breast cancer survival rates are achieved nowadays, irrespective of type of surgery performed, prediction of long‐term physical, sexual, and psychosocial outcomes is very important in treatment decision‐making. Patient‐reported outcomes (PROs) can help facilitate this shared decision‐making....
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7318611/ https://www.ncbi.nlm.nih.gov/pubmed/32160651 http://dx.doi.org/10.1111/tbj.13804 |
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author | van Egdom, Laurentine S. E. Pusic, Andrea Verhoef, Cornelis Hazelzet, Jan A. Koppert, Linetta B. |
author_facet | van Egdom, Laurentine S. E. Pusic, Andrea Verhoef, Cornelis Hazelzet, Jan A. Koppert, Linetta B. |
author_sort | van Egdom, Laurentine S. E. |
collection | PubMed |
description | As high breast cancer survival rates are achieved nowadays, irrespective of type of surgery performed, prediction of long‐term physical, sexual, and psychosocial outcomes is very important in treatment decision‐making. Patient‐reported outcomes (PROs) can help facilitate this shared decision‐making. Given the significance of more personalized medicine and the growing trend on the application of machine learning techniques, we are striving to develop an algorithm using machine learning techniques to predict PROs in breast cancer patients treated with breast surgery. This short communication describes the bottlenecks in our attempt to predict PROs. |
format | Online Article Text |
id | pubmed-7318611 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-73186112020-06-29 Machine learning with PROs in breast cancer surgery; caution: Collecting PROs at baseline is crucial van Egdom, Laurentine S. E. Pusic, Andrea Verhoef, Cornelis Hazelzet, Jan A. Koppert, Linetta B. Breast J Short Communication As high breast cancer survival rates are achieved nowadays, irrespective of type of surgery performed, prediction of long‐term physical, sexual, and psychosocial outcomes is very important in treatment decision‐making. Patient‐reported outcomes (PROs) can help facilitate this shared decision‐making. Given the significance of more personalized medicine and the growing trend on the application of machine learning techniques, we are striving to develop an algorithm using machine learning techniques to predict PROs in breast cancer patients treated with breast surgery. This short communication describes the bottlenecks in our attempt to predict PROs. John Wiley and Sons Inc. 2020-03-11 2020-06 /pmc/articles/PMC7318611/ /pubmed/32160651 http://dx.doi.org/10.1111/tbj.13804 Text en © 2020 The Authors. The Breast Journal published by Wiley Periodicals, Inc. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Short Communication van Egdom, Laurentine S. E. Pusic, Andrea Verhoef, Cornelis Hazelzet, Jan A. Koppert, Linetta B. Machine learning with PROs in breast cancer surgery; caution: Collecting PROs at baseline is crucial |
title | Machine learning with PROs in breast cancer surgery; caution: Collecting PROs at baseline is crucial |
title_full | Machine learning with PROs in breast cancer surgery; caution: Collecting PROs at baseline is crucial |
title_fullStr | Machine learning with PROs in breast cancer surgery; caution: Collecting PROs at baseline is crucial |
title_full_unstemmed | Machine learning with PROs in breast cancer surgery; caution: Collecting PROs at baseline is crucial |
title_short | Machine learning with PROs in breast cancer surgery; caution: Collecting PROs at baseline is crucial |
title_sort | machine learning with pros in breast cancer surgery; caution: collecting pros at baseline is crucial |
topic | Short Communication |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7318611/ https://www.ncbi.nlm.nih.gov/pubmed/32160651 http://dx.doi.org/10.1111/tbj.13804 |
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