ArticleIEEE/ACM transactions on computational biology and bioinformatics
LitMC-BERT: Transformer-Based Multi-Label Classification of Biomedical Literature With An Application on COVID-19 Literature Curation.
Article in IEEE/ACM transactions on computational biology and bioinformatics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning models for abstract screening task - A systematic literature review application for health economics and outcome research.BMC medical research methodology · 2024Pooled it
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- Review
- Machine Learning-Based Identification of Natural History Studies in Rare Diseases: A Step toward Understanding Disease Development and Outcome.Journal of rare diseases (Berlin, Germany) · 2025Article
- Use of deep learning-based NLP models for full-text data elements extraction for systematic literature review tasks.Scientific reports · 2025Article
- Article
- Benchmarking large language models for biomedical natural language processing applications and recommendations.Nature communications · 2025Article
- Accelerating Evidence Synthesis in Observational Studies: Development of a Living Natural Language Processing-Assisted Intelligent Systematic Literature Review System.JMIR medical informatics · 2024Article
- Is metadata of articles about COVID-19 enough for multilabel topic classification task?Database : the journal of biological databases and curation · 2024Article
- Automating Fault Test Cases Generation and Execution for Automotive Safety Validation via NLP and HIL Simulation.Sensors (Basel, Switzerland) · 2024Article
- Heterogeneous deep graph convolutional network with citation relational BERT for COVID-19 inline citation recommendation.Expert systems with applications · 2023Article
- LitCovid in 2022: an information resource for the COVID-19 literature.Nucleic acids research · 2023Article
- Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations.Database : the journal of biological databases and curation · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
The rapid growth of biomedical literature poses a significant challenge for curation and interpretation. This has become more evident during the COVID-19 pandemic. LitCovid, a literature database of COVID-19 related papers in PubMed, has accumulated over 200,000 articles with millions of accesses. Approximately 10,000 new articles are added to LitCovid every month. A main curation task in LitCovid is topic annotation where an article is assigned with up to eight topics, e.g., Treatment and Diagnosis. The annotated topics have been widely used both in LitCovid (e.g., accounting for ∼18% of total uses) and downstream studies such as network generation. However, it has been a primary curation bottleneck due to the nature of the task and the rapid literature growth. This study proposes LITMC-BERT, a transformer-based multi-label classification method in biomedical literature. It uses a shared transformer backbone for all the labels while also captures label-specific features and the correlations between label pairs. We compare LITMC-BERT with three baseline models on two datasets. Its micro-F1 and instance-based F1 are 5% and 4% higher than the current best results, respectively, and only requires ∼18% of the inference time than the Binary BERT baseline. The related datasets and models are available via https://github.com/ncbi/ml-transformer.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.