An overview of biomedical entity linking throughout the years

French, Evan; McInnes, Bridget T.

doi:10.1016/j.jbi.2022.104252

Cited by 20 publications

(10 citation statements)

References 71 publications

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“…To build the BioWiC instances, we considered two main challenges of biomedical texts: semantic and lexical ambiguities. The presence of semantically ambiguous terms , that is, terms that can have multiple meanings in different contexts, is one of the most difficult aspects of biomedical text processing 3 . For instance, the term s taph can be used either as a type of disease (usually followed by infection) or bacteria in other contexts.…”

Section: Methodsmentioning

confidence: 99%

“…However, the extraction of knowledge from these free-text sources is a challenging task as it requires the ability to understand the meaning of natural language and the idiosyncrasies of the biomedical domain but also due to the volume of the data 1 . Biomedical natural language processing (NLP) techniques have been used to analyze information from free-text sources at scale, enabling the extraction and synthesis of biomedical information, and transforming unstructured data into a structured format 2,3 .…”

Section: Background and Summarymentioning

confidence: 99%

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BioWiC: An Evaluation Benchmark for Biomedical Concept Representation

Rouhizadeh,

Nikishina,

Yazdani

et al. 2023

Preprint

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Due to the complexity of the biomedical domain, the ability to capture semantically meaningful representations of terms in context is a long-standing challenge. Despite important progress in the past years, no evaluation benchmark has been developed to evaluate how well language models represent biomedical concepts according to their corresponding context. Inspired by the Word-in-Context (WiC) benchmark, in which word sense disambiguation is reformulated as a binary classification task, we propose a novel dataset, BioWiC, to evaluate the ability of language models to encode biomedical terms in context. We evaluate BioWiC both intrinsically and extrinsically and show that it could be used as a reliable benchmark for evaluating context-dependent embeddings in biomedical corpora. In addition, we conduct several experiments using a variety of discriminative and generative large language models to establish robust baselines that can serve as a foundation for future research.

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Section: Methodsmentioning

confidence: 99%

Section: Background and Summarymentioning

confidence: 99%

BioWiC: An Evaluation Benchmark for Biomedical Concept Representation

Rouhizadeh,

Nikishina,

Yazdani

et al. 2023

Preprint

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“…Overall, the resources for BEL in LoE are extremely scarce. According to a recent review on BEL [67], there are only 3 BEL corpora available in LoE: one in French [68] and two in Spanish [69,70]. Such scarcity of data has a profound negative effect of BEL in LoE.…”

Section: Named Entity Recognition Normalization and Linking For Loementioning

confidence: 99%

Exploring the Latest Highlights in Medical Natural Language Processing across Multiple Languages: A Survey

Shaitarova,

Zaghir,

Lavelli

et al. 2023

Yearb Med Inform

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Objectives: This survey aims to provide an overview of the current state of biomedical and clinical Natural Language Processing (NLP) research and practice in Languages other than English (LoE). We pay special attention to data resources, language models, and popular NLP downstream tasks. Methods: We explore the literature on clinical and biomedical NLP from the years 2020-2022, focusing on the challenges of multilinguality and LoE. We query online databases and manually select relevant publications. We also use recent NLP review papers to identify the possible information lacunae. Results: Our work confirms the recent trend towards the use of transformer-based language models for a variety of NLP tasks in medical domains. In addition, there has been an increase in the availability of annotated datasets for clinical NLP in LoE, particularly in European languages such as Spanish, German and French. Common NLP tasks addressed in medical NLP research in LoE include information extraction, named entity recognition, normalization, linking, and negation detection. However, there is still a need for the development of annotated datasets and models specifically tailored to the unique characteristics and challenges of medical text in some of these languages, especially low-resources ones. Lastly, this survey highlights the progress of medical NLP in LoE, and helps at identifying opportunities for future research and development in this field.

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Section: Introductionmentioning

confidence: 99%

“…Based on French and McInnes (2023) , two major types of entity normalization have been established, namely multi-pass (or multi-step) algorithms and deep learning algorithms. Multi-pass algorithms include abbreviations, synonyms, and/or derivational variants that reflect the use of words in written text ( French and McInnes 2023 ). For example, for the UMLS metathesaurus, different multi-pass approaches have been established to extract concepts from text, ranging from MetaMap ( Aronson 2001 ) to QuickUMLS ( Soldaini and Goharian 2016 ).…”

Section: Introductionmentioning

confidence: 99%

preon: Fast and accurate entity normalization for drug names and cancer types in precision oncology

Ermshaus,

Piechotta,

Rüter

et al. 2024

Bioinformatics

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Motivation In precision oncology (PO), clinicians aim to find the best treatment for any patient based on their molecular characterization. A major bottleneck is the manual annotation and evaluation of individual variants, for which usually a range of knowledge bases are screened. To incorporate and integrate the vast information of different databases, fast and accurate methods for harmonizing databases with different types of information are necessary. An essential step for harmonization in PO includes the normalization of tumor entities as well as therapy options for patients. Summary preon is a fast and accurate library for the normalization of drug names and cancer types in large-scale data integration. Availability and implementation preon is implemented in Python and freely available via the PyPI repository. Source code and the data underlying this article are available in GitHub at https://github.com/ermshaua/preon/.

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An overview of biomedical entity linking throughout the years

Cited by 20 publications

References 71 publications

BioWiC: An Evaluation Benchmark for Biomedical Concept Representation

BioWiC: An Evaluation Benchmark for Biomedical Concept Representation

Exploring the Latest Highlights in Medical Natural Language Processing across Multiple Languages: A Survey

preon: Fast and accurate entity normalization for drug names and cancer types in precision oncology

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