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. 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.
|Keywords||breast cancer surgery, machine learning, patient-reported outcomes|
|Persistent URL||dx.doi.org/10.1111/tbj.13804, hdl.handle.net/1765/125498|
|Journal||The Breast Journal|
van Egdom, L.S.E, Pusic, A. (Andrea), Verhoef, C, Hazelzet, J.A, & Koppert, L.B. (Linetta B.). (2020). Machine learning with PROs in breast cancer surgery; caution: Collecting PROs at baseline is crucial. The Breast Journal. doi:10.1111/tbj.13804