Abstract
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.
Highlights
KEYWORDS breast cancer surgery, machine learning, patient-reported outcomes
Collaboration of the International Consortium for Health Outcomes Measurement (ICHOM) with several other health care institutions worldwide has resulted in the development of a Standard Set for breast cancer outcomes.[10]
Given the significance of more personalized medicine and the growing trend on the application of machine learning techniques, our breast cancer team is striving to develop an algorithm using machine learning techniques to predict Patient-reported outcomes (PROs) in breast cancer patients treated with breast surgery
Summary
KEYWORDS breast cancer surgery, machine learning, patient-reported outcomes Collaboration of the International Consortium for Health Outcomes Measurement (ICHOM) with several other health care institutions worldwide has resulted in the development of a Standard Set for breast cancer outcomes.[10] Within this outcome set, patient-reported outcome measures (PROMs) are pivotal and accounting for 75% of the outcomes evaluated.[10]
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