ArticleHealthcare informatics research2023
Named Entity Recognition in Electronic Health Records: A Methodological Review.
Article in Healthcare informatics research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 43 citations in OpenAlex.
- The use of natural language processing for the identification of ageing syndromes including sarcopenia, frailty and falls in electronic healthcare records: a systematic review.Age and ageing · 2024Pooled it
- Artificial Intelligence in Pharmaceutical Regulatory Science: Opportunities, Challenges, and Emerging Frameworks.The AAPS journal · 2026Review
- Multilingual Evidence-Based Question-Answering for Stroke Discharge Summaries: Study of Cross-Lingual Heterogeneity in Clinical Reports.Journal of medical Internet research · 2026Article
- A joint span-entity prediction approach with generative and cross-lingual meta-learning for low-resource Japanese NER.Scientific reports · 2026Article
- AI-Driven Patient Screening for Clinical Trials in Pancreatic Cancer: The PANCR-AI Pilot Retrospective Comparative Study.JMIR cancer · 2026Article
- Fine-tuning foundational models to code diagnoses from veterinary health records.PLOS digital health · 2026Article
- Leveraging large language models for rare disease named entity recognition.PLOS digital health · 2026Article
- Rethinking sepsis prediction in the era of large language models.npj health systems · 2026Article
- Artificial intelligence-assisted medical coding and DRG management: current applications, challenges, and future perspectives.Frontiers in medicine · 2026Review
- Fine-tuning of language models for automated structuring of medical exam reports to improve patient screening and analysis.Scientific reports · 2025Article
- Cross-institutional dental electronic health record entity extraction via generative artificial intelligence and synthetic notes.JAMIA open · 2025Article
- Advancing named entity recognition in interprofessional collaboration and education.Frontiers in medicine · 2025Article
- Optimized BERT-based NLP outperforms zero-shot methods for automated symptom detection in clinical practice.Frontiers in digital health · 2025Article
- Identifying health conditions in older adults in textual health records using deep learning-based natural language processing.Computational and structural biotechnology journal · 2025Article
- Enhancing Named Entity Recognition for immunology and immune-mediated disorders.Frontiers in immunology · 2025Article
- Evaluating Medical Entity Recognition in Health Care: Entity Model Quantitative Study.JMIR medical informatics · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
objectivesA substantial portion of the data contained in Electronic Health Records (EHR) is unstructured, often appearing as free text. This format restricts its potential utility in clinical decision-making. Named entity recognition (NER) methods address the challenge of extracting pertinent information from unstructured text. The aim of this study was to outline the current NER methods and trace their evolution from 2011 to 2022.
methodsWe conducted a methodological literature review of NER methods, with a focus on distinguishing the classification models, the types of tagging systems, and the languages employed in various corpora.
resultsSeveral methods have been documented for automatically extracting relevant information from EHRs using natural language processing techniques such as NER and relation extraction (RE). These methods can automatically extract concepts, events, attributes, and other data, as well as the relationships between them. Most NER studies conducted thus far have utilized corpora in English or Chinese. Additionally, the bidirectional encoder representation from transformers using the BIO tagging system architecture is the most frequently reported classification scheme. We discovered a limited number of papers on the implementation of NER or RE tasks in EHRs within a specific clinical domain.
conclusionsEHRs play a pivotal role in gathering clinical information and could serve as the primary source for automated clinical decision support systems. However, the creation of new corpora from EHRs in specific clinical domains is essential to facilitate the swift development of NER and RE models applied to EHRs for use in clinical practice.
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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.