ABSTRACT
Machine learning is a powerful tool that could be used for necrotizing enterocolitis research and clinical care. It creates novel diagnostic and prognostic models by analyzing patterns in large, complex databases. From identifying unique microbial signatures to predicting surgical progression of disease, machine learning promises to improve our understanding of NEC. That said, the existence of inferior-quality data, poor model interpretability, and barriers to implementation limit the potential of machine learning algorithms. As machine learning gains traction in modern medicine, we need to address these challenges to advance NEC research and clinical practice.
