ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
A Comparison between Human and NLP-based Annotation of Clinical Trial Eligibility Criteria Text Using The OMOP Common Data Model.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Artificial intelligence in clinical trial participant recruitment and retention: A scoping review and meta-analysis.Journal of clinical and translational science · 2026Review
- Natural Language Processing in Support of Evidence-based Medicine: A Scoping Review.Findings of ACL. ACL · 2025Article
- Decoding Recurrence in Early-Stage and Locoregionally Advanced Non-Small Cell Lung Cancer: Insights From Electronic Health Records and Natural Language Processing.JCO clinical cancer informatics · 2025Article
- CriteriaMapper: establishing the automatic identification of clinical trial cohorts from electronic health records by matching normalized eligibility criteria and patient clinical characteristics.Scientific reports · 2024Article
- 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
- A Framework for Systematic Assessment of Clinical Trial Population Representativeness Using Electronic Health Records Data.Applied clinical informatics · 2021Article
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Authors and funding
9 authors.
Funding
Abstract
Human annotations are the established gold standard for evaluating natural language processing (NLP) methods. The goals of this study are to quantify and qualify the disagreement between human and NLP. We developed an NLP system for annotating clinical trial eligibility criteria text and constructed a manually annotated corpus, both following the OMOP Common Data Model (CDM). We analyzed the discrepancies between the human and NLP annotations and their causes (e.g., ambiguities in concept categorization and tacit decisions on inclusion of qualifiers and temporal attributes during concept annotation). This study initially reported complexities in clinical trial eligibility criteria text that complicate NLP and the limitations of the OMOP CDM. The disagreement between and human and NLP annotations may be generalizable. We discuss implications for NLP evaluation.
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Identifiers
34457154PMC8378608What 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.