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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....

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Detalles Bibliográficos
Autores principales: van Egdom, Laurentine S. E., Pusic, Andrea, Verhoef, Cornelis, Hazelzet, Jan A., Koppert, Linetta B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
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.
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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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