ArticleBMC medical informatics and decision making2017
An active learning-enabled annotation system for clinical named entity recognition.
Article in BMC medical informatics and decision making, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Inference Gap in Domain Expertise and Machine Intelligence in Named Entity Recognition: Creation of and Insights from a Substance Use-related Dataset.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2026Article
- Utilizing active learning strategies in machine-assisted annotation for clinical named entity recognition: a comprehensive analysis considering annotation costs and target effectiveness.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Sample Size Considerations for Fine-Tuning Large Language Models for Named Entity Recognition Tasks: Methodological Study.JMIR AI · 2024Article
- A study of deep active learning methods to reduce labelling efforts in biomedical relation extraction.PloS one · 2023Article
- A Systematic Approach to Configuring MetaMap for Optimal Performance.Methods of information in medicine · 2022Article
- Social and Behavioral Determinants of Health in the Era of Artificial Intelligence with Electronic Health Records: A Scoping Review.Health data science · 2021Article
- Annotating social determinants of health using active learning, and characterizing determinants using neural event extraction.Journal of biomedical informatics · 2021Article
- Clinical concept extraction: A methodology review.Journal of biomedical informatics · 2020Review
- Cost-aware active learning for named entity recognition in clinical text.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- Cost-sensitive Active Learning for Phenotyping of Electronic Health Records.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2019Article
- Article
- Introduction: the International Conference on Intelligent Biology and Medicine (ICIBM) 2016: special focus on medical informatics and big data.BMC medical informatics and decision making · 2017Article
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Authors and funding
11 authors.
Funding
Abstract
backgroundActive learning (AL) has shown the promising potential to minimize the annotation cost while maximizing the performance in building statistical natural language processing (NLP) models. However, very few studies have investigated AL in a real-life setting in medical domain.
methodsIn this study, we developed the first AL-enabled annotation system for clinical named entity recognition (NER) with a novel AL algorithm. Besides the simulation study to evaluate the novel AL algorithm, we further conducted user studies with two nurses using this system to assess the performance of AL in real world annotation processes for building clinical NER models.
resultsThe simulation results show that the novel AL algorithm outperformed traditional AL algorithm and random sampling. However, the user study tells a different story that AL methods did not always perform better than random sampling for different users.
conclusionsWe found that the increased information content of actively selected sentences is strongly offset by the increased time required to annotate them. Moreover, the annotation time was not considered in the querying algorithms. Our future work includes developing better AL algorithms with the estimation of annotation time and evaluating the system with larger number of users.
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