Five Steps in Performing Machine Learning for Binary Outcomes.

OBJECTIVES: Machine learning is evolving quickly in cardiovascular and thoracic surgery. Maximizing the capabilities of machine learning can help to improve patient risk stratification and clinical decision-making, improve accuracy of predictions, and help improve resource utilization in cardiac surgery. There are many nuances and intricacies in machine learning modeling which need to be understood to appropriately implement these technologies in the clinical research setting. Therefore, the goal of this primer is to provide an educational framework of machine learning for generating predicted probabilities in clinical research and to illustrate it with a real-world clinical example.

METHODS: We focus on modeling for binary classification and imbalanced classes as this is a common scenario in cardiothoracic surgery research. We present a 5-step strategy for successfully harnessing the power of machine learning and performing such analyses, and we demonstrate our strategy using a real-world example based on data from the National Surgical Quality Improvement Program (NSQIP) Pediatric database.

CONCLUSIONS: Collaboration between surgeons, care providers, statisticians, data scientists, and information technology professionals can help to maximize the impact of machine learning as a powerful tool in cardiac surgery.