ArticleInternational journal of environmental research and public health2021
A Weakly-Supervised Named Entity Recognition Machine Learning Approach for Emergency Medical Services Clinical Audit.
Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- AI applications in disaster governance with health approach: A scoping review.Archives of public health = Archives belges de sante publique · 2025Pooled it
- Use of artificial intelligence in the out-of-hospital care settings: a scoping review.BMJ digital health & AI · 2026Article
- Leveraging large language models for knowledge-free weak supervision in clinical natural language processing.Scientific reports · 2025Article
- Artificial intelligence in healthcare text processing: a review applied to named entity recognition.Frontiers in artificial intelligence · 2025Review
- Leveraging Large Language Models for Knowledge-free Weak Supervision in Clinical Natural Language Processing.Research square · 2024Article
- The Role of Large Language Models in Transforming Emergency Medicine: Scoping Review.JMIR medical informatics · 2024Article
Corrections and comments
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Authors and funding
9 authors.
Funding
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Abstract
Clinical performance audits are routinely performed in Emergency Medical Services (EMS) to ensure adherence to treatment protocols, to identify individual areas of weakness for remediation, and to discover systemic deficiencies to guide the development of the training syllabus. At present, these audits are performed by manual chart review, which is time-consuming and laborious. In this paper, we report a weakly-supervised machine learning approach to train a named entity recognition model that can be used for automatic EMS clinical audits. The dataset used in this study contained 58,898 unlabeled ambulance incidents encountered by the Singapore Civil Defence Force from 1st April 2019 to 30th June 2019. With only 5% labeled data, we successfully trained three different models to perform the NER task, achieving F1 scores of around 0.981 under entity type matching evaluation and around 0.976 under strict evaluation. The BiLSTM-CRF model was 1~2 orders of magnitude lighter and faster than our BERT-based models. Our proposed proof-of-concept approach may improve the efficiency of clinical audits and can also help with EMS database research. Further external validation of this approach is needed.
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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.