ArticleJournal of the American Medical Informatics Association : JAMIA2016
Active learning: a step towards automating medical concept extraction.
Article in Journal of the American Medical Informatics Association : JAMIA, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Knowledge discovery of diseases symptoms and rehabilitation measures in Q&A communities.Scientific reports · 2025Article
- Capturing Requirements for a Data Annotation Tool for Intensive Care: Experimental User-Centered Design Study.JMIR human factors · 2025Article
- 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
- Natural Language Processing Accurately Differentiates Cancer Symptom Information in Electronic Health Record Narratives.JCO clinical cancer informatics · 2024Article
- Disclosure control of machine learning models from trusted research environments (TRE): New challenges and opportunities.Heliyon · 2023Review
- Disease- and Drug-Related Knowledge Extraction for Health Management from Online Health Communities Based on BERT-BiGRU-ATT.International journal of environmental research and public health · 2022Article
- Machine Learning Models Predicting Cardiovascular and Renal Outcomes and Mortality in Patients with Hyperkalemia.Nutrients · 2022Article
- Strategies to Address the Lack of Labeled Data for Supervised Machine Learning Training With Electronic Health Records: Case Study for the Extraction of Symptoms From Clinical Notes.JMIR medical informatics · 2022Article
- Deep active learning for classifying cancer pathology reports.BMC bioinformatics · 2021Article
- Clinical Text Data in Machine Learning: Systematic Review.JMIR medical informatics · 2020Review
- Cost-aware active learning for named entity recognition in clinical text.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- Precursor-induced conditional random fields: connecting separate entities by induction for improved clinical named entity recognition.BMC medical informatics and decision making · 2019Article
- Minimalistic Approach to Coreference Resolution in Lithuanian Medical Records.Computational and mathematical methods in medicine · 2019Article
- Article
- Clinical Document Classification Using Labeled and Unlabeled Data Across Hospitals.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2018Article
- Using Active Learning to Identify Health Information Technology Related Patient Safety Events.Applied clinical informatics · 2017Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis paper presents an automatic, active learning-based system for the extraction of medical concepts from clinical free-text reports. Specifically, (1) the contribution of active learning in reducing the annotation effort and (2) the robustness of incremental active learning framework across different selection criteria and data sets are determined. MATERIALS AND
methodsThe comparative performance of an active learning framework and a fully supervised approach were investigated to study how active learning reduces the annotation effort while achieving the same effectiveness as a supervised approach. Conditional random fields as the supervised method, and least confidence and information density as 2 selection criteria for active learning framework were used. The effect of incremental learning vs standard learning on the robustness of the models within the active learning framework with different selection criteria was also investigated. The following 2 clinical data sets were used for evaluation: the Informatics for Integrating Biology and the Bedside/Veteran Affairs (i2b2/VA) 2010 natural language processing challenge and the Shared Annotated Resources/Conference and Labs of the Evaluation Forum (ShARe/CLEF) 2013 eHealth Evaluation Lab.
resultsThe annotation effort saved by active learning to achieve the same effectiveness as supervised learning is up to 77%, 57%, and 46% of the total number of sequences, tokens, and concepts, respectively. Compared with the random sampling baseline, the saving is at least doubled.
conclusionIncremental active learning is a promising approach for building effective and robust medical concept extraction models while significantly reducing the burden of manual annotation.
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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.