Adversarial Learning of Knowledge Embeddings for the Unified Medical Language System

Ramon Maldonado; Meliha Yetisgen; Sanda M Harabagiu

Adversarial Learning of Knowledge Embeddings for the Unified Medical Language System

AMIA Jt Summits Transl Sci Proc. 2019 May 6:2019:543-552. eCollection 2019.

Authors

Ramon Maldonado¹, Meliha Yetisgen², Sanda M Harabagiu¹

Affiliations

¹ University of Texas at Dallas, Richardson, TX, USA.
² University of Washington, Seattle, WA, USA.

PMID: 31259009
PMCID: PMC6568073

Abstract

Incorporating the knowledge encoded in the Unified Medical Language System (UMLS) in deep learning methods requires learning knowledge embeddings from the knowledge graphs available in UMLS: the Metathesaurus and the Semantic Network. In this paper we present a technique using Generative Adversarial Networks (GANs) for learning UMLS embeddings and showcase their usage in a clinical prediction model. When the UMLS embeddings are available, the predictions improve by up to 6.9% absolute F₁ score.