ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2014
Using Anchors to Estimate Clinical State without Labeled Data.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
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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
26 citing papers in PubMed.
- Using large language models to enhance clinically-driven missing data recovery algorithms in electronic health records.JAMIA open · 2026Article
- Overcoming data challenges through enriched validation and targeted sampling to measure whole-person health in electronic health records.Journal of biomedical informatics · 2025Article
- Exploring a global interpretation mechanism for deep learning networks when predicting sepsis.Scientific reports · 2023Article
- A semi-supervised adaptive Markov Gaussian embedding process (SAMGEP) for prediction of phenotype event times using the electronic health record.Scientific reports · 2022Article
- Machine Learning in Causal Inference: Application in Pharmacovigilance.Drug safety · 2022Review
- Artificial intelligence projects in healthcare: 10 practical tips for success in a clinical environment.BMJ health & care informatics · 2021Review
- Generative transfer learning for measuring plausibility of EHR diagnosis records.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Amplifying Domain Expertise in Clinical Data Pipelines.JMIR medical informatics · 2020Article
- sureLDA: A multidisease automated phenotyping method for the electronic health record.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Phenotyping issues for exploring electronic health records to design clinical trials.Clinical trials (London, England) · 2020Article
- 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
- Rethinking drug design in the artificial intelligence era.Nature reviews. Drug discovery · 2020Review
- Detecting Social and Behavioral Determinants of Health with Structured and Free-Text Clinical Data.Applied clinical informatics · 2020Article
- Using Natural Language Processing and Machine Learning to Identify Hospitalized Patients with Opioid Use Disorder.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2020Article
- A maximum likelihood approach to electronic health record phenotyping using positive and unlabeled patients.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Automated deep-neural-network surveillance of cranial images for acute neurologic events.Nature medicine · 2018Article
- Advances in Electronic Phenotyping: From Rule-Based Definitions to Machine Learning Models.Annual review of biomedical data science · 2018Article
- High-fidelity phenotyping: richness and freedom from bias.Journal of the American Medical Informatics Association : JAMIA · 2018Article
- Estimating summary statistics for electronic health record laboratory data for use in high-throughput phenotyping algorithms.Journal of biomedical informatics · 2018Article
- Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives.PloS one · 2018Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
We present a novel framework for learning to estimate and predict clinical state variables without labeled data. The resulting models can used for electronic phenotyping, triggering clinical decision support, and cohort selection. The framework relies on key observations which we characterize and term "anchor variables". By specifying anchor variables, an expert encodes a certain amount of domain knowledge about the problem while the rest of learning proceeds in an unsupervised manner. The ability to build anchors upon standardized ontologies and the framework's ability to learn from unlabeled data promote generalizability across institutions. We additionally develop a user interface to enable experts to choose anchor variables in an informed manner. The framework is applied to electronic medical record-based phenotyping to enable real-time decision support in the emergency department. We validate the learned models using a prospectively gathered set of gold-standard responses from emergency physicians for nine clinically relevant variables.
Indexed as
Identifiers
25954366PMC4419996What 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.