# Can Embeddings Adequately Represent Medical Terminology? New Large-Scale Medical Term Similarity Datasets Have the Answer!

arXiv: https://arxiv.org/abs/2003.11082 · HTML: https://mlsanity.com/paper/2003.11082/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Mar 2020.

## Built on by (most cited first, 3 of 3)

- [A Hybrid Approach to Measure Semantic Relatedness in Biomedical Concepts](https://mlsanity.com/paper/2101.10196/) (2021)
- [Transfer Learning in Electronic Health Records through Clinical Concept Embedding](https://mlsanity.com/paper/2107.12919/) (2021)
- [Leveraging knowledge graphs to update scientific word embeddings using latent semantic imputation](https://mlsanity.com/paper/2210.15358/) (2022)

## Abstract (arXiv)

A large number of embeddings trained on medical data have emerged, but it remains unclear how well they represent medical terminology, in particular whether the close relationship of semantically similar medical terms is encoded in these embeddings. To date, only small datasets for testing medical term similarity are available, not allowing to draw conclusions about the generalisability of embeddings to the enormous amount of medical terms used by doctors. We present multiple automatically created large-scale medical term similarity datasets and confirm their high quality in an annotation study with doctors. We evaluate state-of-the-art word and contextual embeddings on our new datasets, comparing multiple vector similarity metrics and word vector aggregation techniques. Our results show that current embeddings are limited in their ability to adequately encode medical terms. The novel datasets thus form a challenging new benchmark for the development of medical embeddings able to accurately represent the whole medical terminology.