ArticleJournal of the American Medical Informatics Association : JAMIA2013
Applying active learning to high-throughput phenotyping algorithms for electronic health records data.
Article in Journal of the American Medical Informatics Association : JAMIA, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers, 1 of them a synthesis that pooled it.
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Who cites it
61 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Extracting information from the text of electronic medical records to improve case detection: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2016Pooled it
- Natural language processing to enhance rheumatoid arthritis care in clinical studies: a scoping review of applications, data, approaches, challenges and future directions.Rheumatology international · 2026Article
- RELEAP: reinforcement-enhanced label-efficient active phenotyping for electronic health records.JAMIA open · 2026Article
- Automated Identification of Cancer-Associated Thrombosis Events via Natural Language Processing: A Systematic Review of the Literature.Bleeding, thrombosis and vascular biology · 2026Article
- Review
- Leveraging undecided cases in chart-reviewed phenotypes to enhance EHR-based association studies.Journal of biomedical informatics · 2025Article
- Artificial intelligence in autoimmune diseases: a bibliometric exploration of the past two decades.Frontiers in immunology · 2025Review
- Generate Analysis-Ready Data for Real-world Evidence: Tutorial for Harnessing Electronic Health Records With Advanced Informatic Technologies.Journal of medical Internet research · 2023Article
- Ontology-driven and weakly supervised rare disease identification from clinical notes.BMC medical informatics and decision making · 2023Article
- Machine Learning in Colorectal Cancer Risk Prediction from Routinely Collected Data: A Review.Diagnostics (Basel, Switzerland) · 2023Review
- Artificial Intelligence in Rheumatoid Arthritis: Current Status and Future Perspectives: A State-of-the-Art Review.Rheumatology and therapy · 2022Review
- Accuracy of identifying hospital acquired venous thromboembolism by administrative coding: implications for big data and machine learning research.Journal of clinical monitoring and computing · 2022Article
- Development of artificial intelligence technology in diagnosis, treatment, and prognosis of colorectal cancer.World journal of gastrointestinal oncology · 2022Review
- Constructing High-Fidelity Phenotype Knowledge Graphs for Infectious Diseases With a Fine-Grained Semantic Information Model: Development and Usability Study.Journal of medical Internet research · 2021Article
- Comparative analysis, applications, and interpretation of electronic health record-based stroke phenotyping methods.BioData mining · 2020Article
- Applications of machine learning to diagnosis and treatment of neurodegenerative diseases.Nature reviews. Neurology · 2020Review
- Development and validation of phenotype classifiers across multiple sites in the observational health data sciences and informatics network.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Polar labeling: silver standard algorithm for training disease classifiers.Bioinformatics (Oxford, England) · 2020Article
- Integrated Random Negative Sampling and Uncertainty Sampling in Active Learning Improve Clinical Drug Safety Drug-Drug Interaction Information Retrieval.Frontiers in pharmacology · 2020Article
- A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases.NPJ digital medicine · 2020Review
1 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
objectivesGeneralizable, high-throughput phenotyping methods based on supervised machine learning (ML) algorithms could significantly accelerate the use of electronic health records data for clinical and translational research. However, they often require large numbers of annotated samples, which are costly and time-consuming to review. We investigated the use of active learning (AL) in ML-based phenotyping algorithms.
methodsWe integrated an uncertainty sampling AL approach with support vector machines-based phenotyping algorithms and evaluated its performance using three annotated disease cohorts including rheumatoid arthritis (RA), colorectal cancer (CRC), and venous thromboembolism (VTE). We investigated performance using two types of feature sets: unrefined features, which contained at least all clinical concepts extracted from notes and billing codes; and a smaller set of refined features selected by domain experts. The performance of the AL was compared with a passive learning (PL) approach based on random sampling.
resultsOur evaluation showed that AL outperformed PL on three phenotyping tasks. When unrefined features were used in the RA and CRC tasks, AL reduced the number of annotated samples required to achieve an area under the curve (AUC) score of 0.95 by 68% and 23%, respectively. AL also achieved a reduction of 68% for VTE with an optimal AUC of 0.70 using refined features. As expected, refined features improved the performance of phenotyping classifiers and required fewer annotated samples.
conclusionsThis study demonstrated that AL can be useful in ML-based phenotyping methods. Moreover, AL and feature engineering based on domain knowledge could be combined to develop efficient and generalizable phenotyping methods.
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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.