Clinical Concept Embeddings Learned from Massive Sources of Multimodal Medical Data

Understand

Word embeddings are a popular approach to unsupervised learning of word relationships that are widely used in natural language processing.

• In this article, we present a new set of embeddings for medical concepts learned using an extremely large collection of multimodal medical data.
• Leaning on recent theoretical insights, we demonstrate how an insurance claims database of 60 million members, a collection of 20 million clinical notes, and 1.7 million full text biomedical journal articles can be combined to embed concepts into a common space, resulting in the largest ever set of embeddings for 108,477 medical concepts.
• To evaluate our approach, we present a new benchmark methodology based on statistical power specifically designed to test embeddings of medical concepts.