Mapping Unparalleled Clinical Professional and Consumer Languages with Embedding Alignment

TL;DR

This paper presents a method for aligning clinical professional and consumer language embeddings using the Procrustes algorithm and adversarial training, aiming to improve translation between medical jargon and layman terms.

Contribution

It introduces an embedding alignment approach for mapping clinical and consumer language, reducing reliance on manual dictionary creation and enhancing scalability.

Findings

Procrustes algorithm effectively aligns professional and consumer language embeddings.

Adversarial training uncovers some relations between the two language types.

Embedding alignment improves semantic mapping between clinical and layman terms.

Abstract

Mapping and translating professional but arcane clinical jargons to consumer language is essential to improve the patient-clinician communication. Researchers have used the existing biomedical ontologies and consumer health vocabulary dictionary to translate between the languages. However, such approaches are limited by expert efforts to manually build the dictionary, which is hard to be generalized and scalable. In this work, we utilized the embeddings alignment method for the word mapping between unparalleled clinical professional and consumer language embeddings. To map semantically similar words in two different word embeddings, we first independently trained word embeddings on both the corpus with abundant clinical professional terms and the other with mainly healthcare consumer terms. Then, we aligned the embeddings by the Procrustes algorithm. We also investigated the approach with the adversarial training with refinement. We evaluated the quality of the alignment through the similar words retrieval both by computing the model precision and as well as judging qualitatively by human. We show that the Procrustes algorithm can be performant for the professional consumer language embeddings alignment, whereas adversarial training with refinement may find some relations between two languages.