Open AccessMachine learning with PROs in breast cancer surgery; caution: Collecting PROs at baseline is crucial
Author(s) -
Egdom Laurentine S. E.,
Pusic Andrea,
Verhoef Cornelis,
Hazelzet Jan A.,
Koppert Linetta B.
Publication year - 2020
Publication title -
the breast journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.533
H-Index - 72
eISSN - 1524-4741
pISSN - 1075-122X
DOI - 10.1111/tbj.13804
Subject(s) - medicine , psychosocial , breast cancer , baseline (sea) , machine learning , artificial intelligence , medical physics , cancer , psychiatry , computer science , oceanography , geology
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.