Machine learning increasingly supports physician decisions by making it easier to detect patterns in data as a means of predicting patient outcomes. In breast cancer, that now could apply to every stage of the experience, from diagnostics to mastectomy and breast reconstruction. At the annual meeting of the American Society of Clinical Oncology -- which was virtual this year, due to the ongoing coronavirus pandemic -- a consortium of researchers presented an abstract detailing how machine learning algorithms were able to correctly predict how individual patients would feel about their breast reconstruction.1 Using this tool in a clinical setting could help physicians guide patients through the recovery process in a way that better anticipates, and subsequently supports, their emotional reaction to this intensely personal medical procedure. Physician-researchers across 11 institutions in the United States and Canada trained 4 different types of machine learning algorithms -- regularized regression, Support Vector Machine, Neural Network, Regression Tree -- to predict with 95% accuracy whether a specific patient would be satisfied or dissatisfied with their breast reconstruction 2 years after their operation.

Machine learning increasingly supports physician decisions by making it easier to detect patterns in data as a means of predicting patient outcomes.