ArticleJournal of biomedical informatics2015
Textual inference for eligibility criteria resolution in clinical trials.
Article in Journal of biomedical informatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed.
- Predicting therapeutic clinical trial enrollment for adult patients with low- and high-grade glioma using supervised machine learning.Science advances · 2025Article
- Automatic extraction of social determinants of health from medical notes of chronic lower back pain patients.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Clinical research staff perceptions on a natural language processing-driven tool for eligibility prescreening: An iterative usability assessment.International journal of medical informatics · 2023Article
- Combining human and machine intelligence for clinical trial eligibility querying.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Musculoskeletal care - at the confluence of data science, sensors, engineering, and computation.BMC musculoskeletal disorders · 2022Review
- Shared-Task Worklists Improve Clinical Trial Recruitment Workflow in an Academic Emergency Department.Applied clinical informatics · 2021Article
- Cohort Selection for Clinical Trials From Longitudinal Patient Records: Text Mining Approach.JMIR medical informatics · 2019Article
- Automatic trial eligibility surveillance based on unstructured clinical data.International journal of medical informatics · 2019Article
- A Curated Cancer Clinical Outcomes Database (C3OD) for accelerating patient recruitment in cancer clinical trials.JAMIA open · 2018Article
- Automatic prediction of coronary artery disease from clinical narratives.Journal of biomedical informatics · 2017Article
- Clinical Research Informatics: Supporting the Research Study Lifecycle.Yearbook of medical informatics · 2017Review
- Aspiring to Unintended Consequences of Natural Language Processing: A Review of Recent Developments in Clinical and Consumer-Generated Text Processing.Yearbook of medical informatics · 2016Review
- Clinical Natural Language Processing in 2015: Leveraging the Variety of Texts of Clinical Interest.Yearbook of medical informatics · 2016Article
- Automatic data source identification for clinical trial eligibility criteria resolution.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2016Article
- Practical applications for natural language processing in clinical research: The 2014 i2b2/UTHealth shared tasks.Journal of biomedical informatics · 2015Article
- Creation of a new longitudinal corpus of clinical narratives.Journal of biomedical informatics · 2015Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Clinical trials are essential for determining whether new interventions are effective. In order to determine the eligibility of patients to enroll into these trials, clinical trial coordinators often perform a manual review of clinical notes in the electronic health record of patients. This is a very time-consuming and exhausting task. Efforts in this process can be expedited if these coordinators are directed toward specific parts of the text that are relevant for eligibility determination. In this study, we describe the creation of a dataset that can be used to evaluate automated methods capable of identifying sentences in a note that are relevant for screening a patient's eligibility in clinical trials. Using this dataset, we also present results for four simple methods in natural language processing that can be used to automate this task. We found that this is a challenging task (maximum F-score=26.25), but it is a promising direction for further research.
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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.