Chemical identification and indexing in full-text articles: an overview of the NLM-Chem track at BioCreative VII

Leaman, Robert; Doğan, Rezarta Islamaj; Adams, Virginia; Alliheedi, Mohammed; Almeida, João Rafael; Antunes, Rui; Bevan, Robert; Chang, Yung Chun; Erdengasileng, Arslan; Hodgskiss, Matthew; Ida, Ryuki; Kim, Hyunjae; Li, Keqiao; Mercer, Robert E.; Mertová, Lukrécia; Mobasher, Ghadeer; Shin, Hoo-Chang; Sung, Mujeen; Tsujimura, Tomoki; Yeh, Wen-Chao; Lu, Zhiyong

doi:10.1093/database/baad005

Database

2023

DOI: 10.1093/database/baad005

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Chemical identification and indexing in full-text articles: an overview of the NLM-Chem track at BioCreative VII

Robert Leaman

Rezarta Islamaj Doğan

Virginia Adams

et al.

Abstract: The BioCreative National Library of Medicine (NLM)-Chem track calls for a community effort to fine-tune automated recognition of chemical names in the biomedical literature. Chemicals are one of the most searched biomedical entities in PubMed, and—as highlighted during the coronavirus disease 2019 pandemic—their identification may significantly advance research in multiple biomedical subfields. While previous community challenges focused on identifying chemical names mentioned in titles and abstracts, the full… Show more

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2024

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Cited by 2 publications

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Towards discovery: an end-to-end system for uncovering novel biomedical relations

Almeida,

Jonker,

Antunes

et al. 2024

Database

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Biomedical relation extraction is an ongoing challenge within the natural language processing community. Its application is important for understanding scientific biomedical literature, with many use cases, such as drug discovery, precision medicine, disease diagnosis, treatment optimization and biomedical knowledge graph construction. Therefore, the development of a tool capable of effectively addressing this task holds the potential to improve knowledge discovery by automating the extraction of relations from research manuscripts. The first track in the BioCreative VIII competition extended the scope of this challenge by introducing the detection of novel relations within the literature. This paper describes that our participation system initially focused on jointly extracting and classifying novel relations between biomedical entities. We then describe our subsequent advancement to an end-to-end model. Specifically, we enhanced our initial system by incorporating it into a cascading pipeline that includes a tagger and linker module. This integration enables the comprehensive extraction of relations and classification of their novelty directly from raw text. Our experiments yielded promising results, and our tagger module managed to attain state-of-the-art named entity recognition performance, with a micro F1-score of 90.24, while our end-to-end system achieved a competitive novelty F1-score of 24.59. The code to run our system is publicly available at https://github.com/ieeta-pt/BioNExt. Database URL: https://github.com/ieeta-pt/BioNExt

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Towards discovery: an end-to-end system for uncovering novel biomedical relations

Almeida,

Jonker,

Antunes

et al. 2024

Database

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Multi-head CRF classifier for biomedical multi-class named entity recognition on Spanish clinical notes

Jonker,

Almeida,

Antunes

et al. 2024

Database

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The identification of medical concepts from clinical narratives has a large interest in the biomedical scientific community due to its importance in treatment improvements or drug development research. Biomedical named entity recognition (NER) in clinical texts is crucial for automated information extraction, facilitating patient record analysis, drug development, and medical research. Traditional approaches often focus on single-class NER tasks, yet recent advancements emphasize the necessity of addressing multi-class scenarios, particularly in complex biomedical domains. This paper proposes a strategy to integrate a multi-head conditional random field (CRF) classifier for multi-class NER in Spanish clinical documents. Our methodology overcomes overlapping entity instances of different types, a common challenge in traditional NER methodologies, by using a multi-head CRF model. This architecture enhances computational efficiency and ensures scalability for multi-class NER tasks, maintaining high performance. By combining four diverse datasets, SympTEMIST, MedProcNER, DisTEMIST, and PharmaCoNER, we expand the scope of NER to encompass five classes: symptoms, procedures, diseases, chemicals, and proteins. To the best of our knowledge, these datasets combined create the largest Spanish multi-class dataset focusing on biomedical entity recognition and linking for clinical notes, which is important to train a biomedical model in Spanish. We also provide entity linking to the multi-lingual Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) vocabulary, with the eventual goal of performing biomedical relation extraction. Through experimentation and evaluation of Spanish clinical documents, our strategy provides competitive results against single-class NER models. For NER, our system achieves a combined micro-averaged F1-score of 78.73, with clinical mentions normalized to SNOMED CT with an end-to-end F1-score of 54.51. The code to run our system is publicly available at https://github.com/ieeta-pt/Multi-Head-CRF. Database URL: https://github.com/ieeta-pt/Multi-Head-CRF

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Chemical identification and indexing in full-text articles: an overview of the NLM-Chem track at BioCreative VII

Cited by 2 publications

References 37 publications

Towards discovery: an end-to-end system for uncovering novel biomedical relations

Towards discovery: an end-to-end system for uncovering novel biomedical relations

Multi-head CRF classifier for biomedical multi-class named entity recognition on Spanish clinical notes

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