ArticleJournal of the American Medical Informatics Association : JAMIA
Active learning for clinical text classification: is it better than random sampling?
Article in Journal of the American Medical Informatics Association : JAMIA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed.
- AI-Based Structured Information Extraction From Synthetic Nursing Handover Transcripts: Comparative Evaluation of Large Language Models.JMIR nursing · 2026Article
- Integration of fairness-awareness into clinical language processing models.Communications medicine · 2026Article
- Rethinking Domain-Specific Pretraining by Supervised or Self-Supervised Learning for Chest Radiograph Classification: A Comparative Study Against ImageNet Counterparts in Cold-Start Active Learning.Health care science · 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
- Active Learning for Multi-way Sensitivity Analysis with Application to Disease Screening Modeling.Journal of healthcare informatics research · 2022Article
- When BERT meets Bilbo: a learning curve analysis of pretrained language model on disease classification.BMC medical informatics and decision making · 2022Article
- Improving Diabetes-Related Biomedical Literature Exploration in the Clinical Decision-making Process via Interactive Classification and Topic Discovery: Methodology Development Study.Journal of medical Internet research · 2022Article
- Deep active learning for classifying cancer pathology reports.BMC bioinformatics · 2021Article
- Clinical text classification with rule-based features and knowledge-guided convolutional neural networks.BMC medical informatics and decision making · 2019Article
- Article
- Challenges in adapting existing clinical natural language processing systems to multiple, diverse health care settings.Journal of the American Medical Informatics Association : JAMIA · 2017Article
- Inter-labeler and intra-labeler variability of condition severity classification models using active and passive learning methods.Artificial intelligence in medicine · 2017Article
- An active learning-enabled annotation system for clinical named entity recognition.BMC medical informatics and decision making · 2017Article
- Improving condition severity classification with an efficient active learning based framework.Journal of biomedical informatics · 2016Article
- Active learning: a step towards automating medical concept extraction.Journal of the American Medical Informatics Association : JAMIA · 2016Article
- A study of active learning methods for named entity recognition in clinical text.Journal of biomedical informatics · 2015Article
- TextHunter--A User Friendly Tool for Extracting Generic Concepts from Free Text in Clinical Research.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2014Article
- Virk: an active learning-based system for bootstrapping knowledge base development in the neurosciences.Frontiers in neuroinformatics · 2013Article
- Using statistical text classification to identify health information technology incidents.Journal of the American Medical Informatics Association : JAMIAArticle
- Supervised machine learning and active learning in classification of radiology reports.Journal of the American Medical Informatics Association : JAMIAArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
objectiveThis study explores active learning algorithms as a way to reduce the requirements for large training sets in medical text classification tasks.
designThree existing active learning algorithms (distance-based (DIST), diversity-based (DIV), and a combination of both (CMB)) were used to classify text from five datasets. The performance of these algorithms was compared to that of passive learning on the five datasets. We then conducted a novel investigation of the interaction between dataset characteristics and the performance results. MEASUREMENTS: Classification accuracy and area under receiver operating characteristics (ROC) curves for each algorithm at different sample sizes were generated. The performance of active learning algorithms was compared with that of passive learning using a weighted mean of paired differences. To determine why the performance varies on different datasets, we measured the diversity and uncertainty of each dataset using relative entropy and correlated the results with the performance differences.
resultsThe DIST and CMB algorithms performed better than passive learning. With a statistical significance level set at 0.05, DIST outperformed passive learning in all five datasets, while CMB was found to be better than passive learning in four datasets. We found strong correlations between the dataset diversity and the DIV performance, as well as the dataset uncertainty and the performance of the DIST algorithm.
conclusionFor medical text classification, appropriate active learning algorithms can yield performance comparable to that of passive learning with considerably smaller training sets. In particular, our results suggest that DIV performs better on data with higher diversity and DIST on data with lower uncertainty.
Indexed as
Identifiers
What OpenQuestion holds
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.