Strengths and Limitations of Machine Learning in Surgical Care

Bertsimas and co-authors1 should be commended on the development of the Predictive Optimal Trees in Emergency Surgery Risk (POTTER) tool, and its most recent application in emergency general surgery.2 The use of machine learning-based risk prediction tools as the basis for this study opens the door to a timely discussion about the role of machine learning approaches in support of decision-making in surgery. The following critical concepts must be explored before the adoption of POTTER and similar models for use in clinical settings: strengths and limitations of machine learning methods, including mechanisms to fairly assess their performance; and the importance of inclusion of hospital or surgeon factors when modeling.
Source: Journal of the American College of Surgeons - Category: Surgery Authors: Tags: Invited Commentary Source Type: research