ArticleNature communications2021
Ontology-driven weak supervision for clinical entity classification in electronic health records.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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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.
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Who cites it
26 citing papers in PubMed.
- A unified framework and benchmark for generalizable biomedical knowledge extraction and applications with large language models.Cell reports. Medicine · 2026Article
- Computable Phenotype for Identifying Undiagnosed Hypermobile Ehlers-Danlos Syndrome: Protocol for a Development and Validation Study.JMIR research protocols · 2026Article
- Scalable Identification of Clinically Relevant Chronic Obstructive Pulmonary Disease Documents in Large-Scale Electronic Health Record Datasets With a Lightweight Natural Language Processing Model: Retrospective Cohort Study.JMIR medical informatics · 2026Article
- A review on knowledge graphs for healthcare: Resources, applications, and promises.Journal of biomedical informatics · 2025Review
- Leveraging large language models for knowledge-free weak supervision in clinical natural language processing.Scientific reports · 2025Article
- Clinical entity augmented retrieval for clinical information extraction.NPJ digital medicine · 2025Article
- Weakly supervised text classification on free-text comments in patient-reported outcome measures.Frontiers in digital health · 2025Article
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- Transformer models in biomedicine.BMC medical informatics and decision making · 2024Review
- Leveraging Large Language Models for Knowledge-free Weak Supervision in Clinical Natural Language Processing.Research square · 2024Article
- Medical-informed machine learning: integrating prior knowledge into medical decision systems.BMC medical informatics and decision making · 2024Review
- Evolution and emerging trends of named entity recognition: Bibliometric analysis from 2000 to 2023.Heliyon · 2024Review
- Scalable Approach to Consumer Wearable Postmarket Surveillance: Development and Validation Study.JMIR medical informatics · 2024Article
- Development and application of Chinese medical ontology for diabetes mellitus.BMC medical informatics and decision making · 2024Article
- The Stanford Medicine data science ecosystem for clinical and translational research.JAMIA open · 2023Article
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- Bottom-up and top-down paradigms of artificial intelligence research approaches to healthcare data science using growing real-world big data.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Not so weak PICO: leveraging weak supervision for participants, interventions, and outcomes recognition for systematic review automation.JAMIA open · 2023Article
- Artificial intelligence in critical illness and its impact on patient care: a comprehensive review.Frontiers in medicine · 2023Review
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7 authors.
Funding
Abstract
In the electronic health record, using clinical notes to identify entities such as disorders and their temporality (e.g. the order of an event relative to a time index) can inform many important analyses. However, creating training data for clinical entity tasks is time consuming and sharing labeled data is challenging due to privacy concerns. The information needs of the COVID-19 pandemic highlight the need for agile methods of training machine learning models for clinical notes. We present Trove, a framework for weakly supervised entity classification using medical ontologies and expert-generated rules. Our approach, unlike hand-labeled notes, is easy to share and modify, while offering performance comparable to learning from manually labeled training data. In this work, we validate our framework on six benchmark tasks and demonstrate Trove's ability to analyze the records of patients visiting the emergency department at Stanford Health Care for COVID-19 presenting symptoms and risk factors.
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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.