ArticleJournal of the American Medical Informatics Association : JAMIA2024
Utilizing active learning strategies in machine-assisted annotation for clinical named entity recognition: a comprehensive analysis considering annotation costs and target effectiveness.
Article in Journal of the American Medical Informatics Association : JAMIA, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Context matching is not reasoning when performing generalized clinical evaluation of generative language models.NPJ digital medicine · 2025Article
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Abstract
objectivesActive learning (AL) has rarely integrated diversity-based and uncertainty-based strategies into a dynamic sampling framework for clinical named entity recognition (NER). Machine-assisted annotation is becoming popular for creating gold-standard labels. This study investigated the effectiveness of dynamic AL strategies under simulated machine-assisted annotation scenarios for clinical NER. MATERIALS AND
methodsWe proposed 3 new AL strategies: a diversity-based strategy (CLUSTER) based on Sentence-BERT and 2 dynamic strategies (CLC and CNBSE) capable of switching from diversity-based to uncertainty-based strategies. Using BioClinicalBERT as the foundational NER model, we conducted simulation experiments on 3 medication-related clinical NER datasets independently: i2b2 2009, n2c2 2018 (Track 2), and MADE 1.0. We compared the proposed strategies with uncertainty-based (LC and NBSE) and passive-learning (RANDOM) strategies. Performance was primarily measured by the number of edits made by the annotators to achieve a desired target effectiveness evaluated on independent test sets.
resultsWhen aiming for 98% overall target effectiveness, on average, CLUSTER required the fewest edits. When aiming for 99% overall target effectiveness, CNBSE required 20.4% fewer edits than NBSE did. CLUSTER and RANDOM could not achieve such a high target under the pool-based simulation experiment. For high-difficulty entities, CNBSE required 22.5% fewer edits than NBSE to achieve 99% target effectiveness, whereas neither CLUSTER nor RANDOM achieved 93% target effectiveness. DISCUSSION AND
conclusionWhen the target effectiveness was set high, the proposed dynamic strategy CNBSE exhibited both strong learning capabilities and low annotation costs in machine-assisted annotation. CLUSTER required the fewest edits when the target effectiveness was set low.
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