Efficient Biomedical Entity Linking: Clinical Text Standardization with Low-Resource Techniques (2024)

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.

## introduction and related work medical text consists of a diverse vocabulary derived from various nomenclatures including varying surface forms corresponding to terms like diagnosis, treatment, medications, etc. this diversity poses a challenge for effective communication * akshit.achara@gehealthcare.com † sanand.sasidharan@gehealthcare.com ‡ gagan.n@gehealthcare.com across medical institutions and organizations. one of the techniques to mitigate this inherent diversity present in multiple references to the same term is entity linking. entity linking is used to map these references to standardized codes. these codes are curated and maintained by medical organizations for standardization of medical nomenclature. given a corpus, entity linking includes the mapping of a mention m which is a span of k words, to an entity ϵ, where the entity belongs to a knowledge base such as wikipedia. in the biomedical domain, the textual phrases are linked with the corresponding concepts from a knowledge base constructed using the medical ontologies like umls @xcite , snomed @xcite , etc. the umls ontology comprises of a broad range of clinical entities along with rich information for each entity like synonyms, definitions, etc. traditional approaches for entity linking, such as support vector machines @xcite and random forests @xcite , rely heavily on hand-crafted features, thereby restricting generalization to diverse data. neural networks have emerged as a prominent technique for entity linking due to their ability to learn semantic representations from textual data. alias matching based techniques like @xcite @xcite have been proposed where an input mention is mapped to an alias associated with an entity in the knowledge-base. however, these techniques require large amount of training data. contextualized entity linking approaches @xcite utilize the semantic similarity between contextualized mentions. this approach requires a list of entities in advance and includes distant-supervision on articles containing examples of these entities. generating medical codes using large language models can be error prone @xcite . in @xcite , the authors use a seq2seq model to map a mention to its canonical entity name. this method is resource intensive and requires generation of synthetic examples for pretraining, utilizing entity definitions and synonyms. in @xcite , the authors propose a zero-shot entity linking approach by leveraging synonym and graph based tasks. however, the approaches require training samples from umls for both these tasks. moreover, entity disambiguation has not been explored in the work. efficient student models like minilm @xcite can be used to perform contrastive learning on synonyms of entities. this results in a significantly less embedding size (384) as compared to the approaches like sapbert @xcite with an embedding size of 768. the predicted candidates in alias based techniques are ranked based on the cosine similarity score. however, there are ambiguous cases where multiple entities have similar scores for a common mention. therefore, there is a requirement to disambiguate these candidates through reranking. cross-attention based reranking approaches utilize supervised training on the concatenated mention and candidate representations as inputs @xcite . more recent approaches utilize homonym disambiguation @xcite and have shown to improve the performance of autoregressive approaches like genbioel. in comparison to the discussed techniques, we propose an efficient and low resource zero-shot biomedical entity linking approach along with a suite of disambiguation techniques. furthermore, we introduce an article level similarity analysis to obtain further insights. this also allows us to conduct a qualitative analysis without manually going through all the articles manually. our contributions are as follows: ## datasets in this work, we explore entity linking on the medmentions @xcite dataset which consists of titles and abstracts from 4392 english biomedical articles. these articles comprise of textual spans annotated with mentions of umls 2017aa entities. the dataset provides two versions: a full version containing 34724 unique entities and an st21pv version with 25419 unique entities, the latter being recommended by the authors for information retrieval. further details about the dataset versions are discussed in ## methodology in this work, we create a prototype vector space comprising of the encodings (feature vectors) associated with the canonical name of each entity in the umls ontology. to obtain meaningful encodings for constructing this prototype space, we train an encoder-based transformer @xcite model on pairs of canonical names of entity synonyms. this is similar to the training approaches utilized in @xcite and @xcite . the prototype space constructed using this trained model is used for performing semantic search, where the query encoding is obtained by passing the mention through the same model. this step is known as candidate generation. the candidate generation may lead to ambiguous results where multiple predicted entities have equal similarity scores. this is addressed through the reranking approaches discussed in section 3.3. finally, we utilize both semantic similarity and retrieval performance for our quantitaive and qualitative evaluation. the comprehensive structure of our proposed approaches is depicted in the figure 1 . the following sub-sections discuss the individual components used in our work: ## results and discussion we obtain the retrieval performance for the discussed approaches by considering the top-k closest candidates (that include aliases) from the prototype space. we observe that the retrieval performance (considering top-128 candidates) of all the miniel and miniel 1000 approaches is around 87% for the st21pv version and 88% for the full version of the medmentions dataset. ## conclusion biomedical entity linking has been an active area of research with various approaches being proposed to improve medical text standardization (see details in section 1). we propose a multi-stage approach where the first stage retrieves candidates with a high recall (∼ 87% for top-128 candidates). this is followed by application of the proposed reranking approaches focused on improving the r@1 retrieval performance. the reranking improves the performance by more than 10% (see figure 2 and