ArticleJournal of biomedical informatics2009
ConText: an algorithm for determining negation, experiencer, and temporal status from clinical reports.
Article in Journal of biomedical informatics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 188 papers, 2 of them syntheses that pooled it.
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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
188 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Secondary use of health records for prediction, detection, and treatment planning in the clinical decision support system: a systematic review.BMC medical informatics and decision making · 2025Pooled 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
- Lumbar Imaging With Reporting Of Epidemiology (LIRE)--Protocol for a pragmatic cluster randomized trial.Contemporary clinical trials · 2015Trial
- Fine-Tuning Large Language Models for Structured Extraction of Infectious Disease-Related Information From Clinical Notes in Japanese Primary Care: Development and Internal Validation Study.JMIR formative research · 2026Article
- Sequential multi-site fine-tuning for incremental deployment of large language models for mobility functional status extraction.JAMIA open · 2026Article
- Identification of Patients for a Community Health Worker Program Using an Artificial Intelligence Algorithm.Learning health systems · 2026Article
- Characterization and comparison of structured and unstructured electronic health record data mapped to MedDRA for post-marketing surveillance.JAMIA open · 2026Article
- Operationalizing Large Language Models for Clinical Research Data Extraction: Methods, Quality Control, and Governance.Journal of medical systems · 2026Review
- Artificial Intelligence for Opioid Safety Surveillance from Clinical Text: A Clinically Focused Review.Journal of clinical medicine · 2026Review
- Using Natural Language Processing to Characterize Early Steps in the Kidney Transplant Evaluation Process Documented in the National Veterans Affairs Electronic Health Record.Clinical transplantation · 2026Article
- Detecting Bicuspid Aortic Valve From Echocardiographic Reports Using Natural Language Processing: A Veterans Affairs Study.JACC. Advances · 2026Article
- Supporting cancer research on real-world data: extracting colorectal cancer status and explicitly written TNM stages from free-text imaging and histopathology reports.BMJ health & care informatics · 2025Article
- Article
- Extracting post-acute sequelae of SARS-CoV-2 infection symptoms from clinical notes via hybrid natural language processing.npj health systems · 2025Article
- medspacyV: a graphical user interface for the open source medspaCy natural language processing package.JAMIA open · 2025Article
- Large Language Model Symptom Identification From Clinical Text: Multicenter Study.Journal of medical Internet research · 2025Article
- Development and Validation of Natural Language Processing Algorithms in the ENACT National Electronic Health Record Research Network.medRxiv : the preprint server for health sciences · 2025Article
- Hybrid natural language processing tool for semantic annotation of medical texts in Spanish.BMC bioinformatics · 2025Article
- Studying Veteran food insecurity longitudinally using electronic health record data and natural language processing.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- Context-Aware Contrastive Representation Learning for Zero-Shot Biomedical Text Classification.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2024Article
128 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors.
Funding
Abstract
In this paper we describe an algorithm called ConText for determining whether clinical conditions mentioned in clinical reports are negated, hypothetical, historical, or experienced by someone other than the patient. The algorithm infers the status of a condition with regard to these properties from simple lexical clues occurring in the context of the condition. The discussion and evaluation of the algorithm presented in this paper address the questions of whether a simple surface-based approach which has been shown to work well for negation can be successfully transferred to other contextual properties of clinical conditions, and to what extent this approach is portable among different clinical report types. In our study we find that ConText obtains reasonable to good performance for negated, historical, and hypothetical conditions across all report types that contain such conditions. Conditions experienced by someone other than the patient are very rarely found in our report set. A comprehensive solution to the problem of determining whether a clinical condition is historical or recent requires knowledge above and beyond the surface clues picked up by ConText.
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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.