ArticlePLOS digital health2022
Large-scale application of named entity recognition to biomedicine and epidemiology.
Article in PLOS digital health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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The trial behind it
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Who cites it
17 citing papers in PubMed, 41 citations in OpenAlex.
- Entity-centric evaluation of large language model responses for medical question-answering tasks.PLOS digital health · 2026Article
- From narrative evidence to computable knowledge: a decision-relevant corpus for medicinal herb-disease relationships.Journal of biomedical semantics · 2026Article
- Improving few-shot named entity recognition for large language models using structured dynamic prompting with retrieval augmented generation.NPJ artificial intelligence · 2026Article
- Utilizing Large Language Models for Zero-Shot Medical Ontology Extension from Clinical Notes.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2025Article
- Premenstrual dysphoric disorder in online peer support communities: a Reddit case study.Scientific reports · 2025Article
- MIMIC-IV-Ext-22MCTS: A 22 Million-Event Temporal Clinical Time-Series Dataset for Risk Prediction.Research square · 2025Article
- Do LLMs Surpass Encoders for Biomedical NER?Proceedings. IEEE International Conference on Healthcare Informatics · 2025Article
- Precision in Parsing: Evaluation of an Open-Source Named Entity Recognizer (NER) in Veterinary Oncology.Veterinary and comparative oncology · 2025Article
- Pet and Stray Dogs' Contribution to Zoonotic Transmission Pathways: A Bibliometric Review.Transboundary and emerging diseases · 2025Review
- Task-Specific Transformer-Based Language Models in Health Care: Scoping Review.JMIR medical informatics · 2024Article
- Hospital Re-Admission Prediction Using Named Entity Recognition and Explainable Machine Learning.Diagnostics (Basel, Switzerland) · 2024Article
- Explanatory argumentation in natural language for correct and incorrect medical diagnoses.Journal of biomedical semantics · 2024Article
- RecSOI: recommending research directions using statements of ignorance.Journal of biomedical semantics · 2024Article
- Harnessing PubMed User Query Logs for Post Hoc Explanations of Recommended Similar Articles.ArXiv · 2024Article
- Automating Clinical Trial Matches Via Natural Language Processing of Synthetic Electronic Health Records and Clinical Trial Eligibility Criteria.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024Article
- Constructing a disease database and using natural language processing to capture and standardize free text clinical information.Scientific reports · 2023Article
- A framework for multi-faceted content analysis of social media chatter regarding non-medical use of prescription medications.BMC digital health · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDespite significant advancements in biomedical named entity recognition methods, the clinical application of these systems continues to face many challenges: (1) most of the methods are trained on a limited set of clinical entities; (2) these methods are heavily reliant on a large amount of data for both pre-training and prediction, making their use in production impractical; (3) they do not consider non-clinical entities, which are also related to patient's health, such as social, economic or demographic factors.
methodsIn this paper, we develop Bio-Epidemiology-NER (https://pypi.org/project/Bio-Epidemiology-NER/) an open-source Python package for detecting biomedical named entities from the text. This approach is based on a Transformer-based system and trained on a dataset that is annotated with many named entities (medical, clinical, biomedical, and epidemiological). This approach improves on previous efforts in three ways: (1) it recognizes many clinical entity types, such as medical risk factors, vital signs, drugs, and biological functions; (2) it is easily configurable, reusable, and can scale up for training and inference; (3) it also considers non-clinical factors (age and gender, race and social history and so) that influence health outcomes. At a high level, it consists of the phases: pre-processing, data parsing, named entity recognition, and named entity enhancement.
resultsExperimental results show that our pipeline outperforms other methods on three benchmark datasets with macro-and micro average F1 scores around 90 percent and above.
conclusionThis package is made publicly available for researchers, doctors, clinicians, and anyone to extract biomedical named entities from unstructured biomedical texts.
Identifiers
What OpenQuestion holds
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.