Embedding Methods for Electronic Health Record Research.

Abstract

This review aims to elucidate the role and impact of embedding techniques in the analysis and utilization of electronic health record data for research. By integrating multidimensional, incongruent, and often unstructured medical data for machine learning models, embeddings provide a powerful tool for enhancing data utility, especially under certain conditions and for asking certain questions. We explore a variety of embedding methods, including but not limited to word embeddings, graph embeddings, and other deep learning models. We highlight key applications of embeddings that are representative of a variety of areas of research, including predictive modeling, patient stratification, clinical decision support, and beyond. Finally, we show how to evaluate the impact and quality of embeddings in real-world clinical settings, assessing their performance against traditional models and noting areas where they deliver substantial improvements or fall short.