A Basic Machine Learning Primer for Surgical Research in Congenital Heart Disease

Abstract

Artificial intelligence and machine learning are rapidly transforming medicine, healthcare, and surgery. Machine learning is a valuable tool for surgeons and researchers in pediatric cardiovascular and thoracic surgery, with innovative applications constantly evolving and expanding. Utilizing machine learning in addition to traditional statistical methods can gain insights into the data and develop more powerful prediction models for improving surgical management and patient outcomes. We provide an accessible introduction to machine learning for surgeons to become familiar with its key essential concepts and architecture, along with a five-step strategy for performing machine learning analyses. With careful study planning using high-quality data, active collaboration between surgeons, researchers, statisticians, and data scientists, and real-world implementation of machine learning algorithms in the clinical setting, machine learning can be a strategic tool for gaining insights into the data in order to improve surgical decision-making, patient risk management, and surgical outcomes.