Efficient Biomedical Entity Linking: Clinical Text Standardization with Low-Resource Techniques

TL;DR

This paper presents a resource-efficient biomedical entity linking method that leverages synonym pairs and reranking techniques, achieving competitive performance without domain-specific training on large datasets.

Contribution

It introduces a low-resource approach for biomedical entity linking using synonym pairs and novel reranking methods, reducing training data and computational requirements.

Findings

Achieves similar performance to state-of-the-art methods on Medmentions dataset

Reduces training data and resource consumption significantly

Provides insights beyond retrieval metrics through detailed analysis

Abstract

Clinical text is rich in information, with mentions of treatment, medication and anatomy among many other clinical terms. Multiple terms can refer to the same core concepts which can be referred as a clinical entity. Ontologies like the Unified Medical Language System (UMLS) are developed and maintained to store millions of clinical entities including the definitions, relations and other corresponding information. These ontologies are used for standardization of clinical text by normalizing varying surface forms of a clinical term through Biomedical entity linking. With the introduction of transformer-based language models, there has been significant progress in Biomedical entity linking. In this work, we focus on learning through synonym pairs associated with the entities. As compared to the existing approaches, our approach significantly reduces the training data and resource consumption. Moreover, we propose a suite of context-based and context-less reranking techniques for performing the entity disambiguation. Overall, we achieve similar performance to the state-of-the-art zero-shot and distant supervised entity linking techniques on the Medmentions dataset, the largest annotated dataset on UMLS, without any domain-based training. Finally, we show that retrieval performance alone might not be sufficient as an evaluation metric and introduce an article level quantitative and qualitative analysis to reveal further insights on the performance of entity linking methods.