ArticleJournal of the American Medical Informatics Association : JAMIA2017
EliIE: An open-source information extraction system for clinical trial eligibility criteria.
Article in Journal of the American Medical Informatics Association : JAMIA, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.
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The trial behind it
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Who cites it
55 citing papers in PubMed.
- AI-based multimodal integration of genomics and electronic health records.Nature reviews. Genetics · 2026Review
- A Natural Language Processing Framework for Structuring and Visualizing Clinical Trial Eligibility Criteria at Scale: Protocol for a Quantitative Study.JMIR research protocols · 2026Article
- A Multi-Model LLM Consensus Framework to Identify EHR-Predictable Eligibility Criteria in NSCLC Immunotherapy Trials.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- Analysis of eligibility criteria clusters based on large language models for clinical trial design.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Artificial Intelligence in Cardiovascular Clinical Trials.Journal of the American College of Cardiology · 2024Review
- 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
- Natural Language Processing for Digital Health in the Era of Large Language Models.Yearbook of medical informatics · 2024Review
- Article
- Criteria2Query 3.0: Leveraging generative large language models for clinical trial eligibility query generation.Journal of biomedical informatics · 2024Article
- AutoCriteria: a generalizable clinical trial eligibility criteria extraction system powered by large language models.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Implementation of inclusion and exclusion criteria in clinical studies in OHDSI ATLAS software.Scientific reports · 2023Article
- Parsing Clinical Trial Eligibility Criteria for Cohort Query by a Multi-Input Multi-Output Sequence Labeling Model.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2023Article
- ClinicalRisk: A New Therapy-related Clinical Trial Dataset for Predicting Trial Status and Failure Reasons.Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management · 2023Article
- A review of research on eligibility criteria for clinical trials.Clinical and experimental medicine · 2023Review
- The IMPACT framework and implementation for accessible in silico clinical phenotyping in the digital era.NPJ digital medicine · 2023Review
- Named Entity Recognition and Normalization for Alzheimer's Disease Eligibility Criteria.Proceedings. IEEE International Conference on Healthcare Informatics · 2023Article
- Piloting an automated clinical trial eligibility surveillance and provider alert system based on artificial intelligence and standard data models.BMC medical research methodology · 2023Observational
- A Scoping Review of Adopted Information Extraction Methods for RCTs.Medical journal of the Islamic Republic of Iran · 2023Article
- Text Classification of Cancer Clinical Trial Eligibility Criteria.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- Artificial Intelligence Applied to clinical trials: opportunities and challenges.Health and technology · 2023Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
objectiveTo develop an open-source information extraction system called Eligibility Criteria Information Extraction (EliIE) for parsing and formalizing free-text clinical research eligibility criteria (EC) following Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) version 5.0. MATERIALS AND
methodsEliIE parses EC in 4 steps: (1) clinical entity and attribute recognition, (2) negation detection, (3) relation extraction, and (4) concept normalization and output structuring. Informaticians and domain experts were recruited to design an annotation guideline and generate a training corpus of annotated EC for 230 Alzheimer's clinical trials, which were represented as queries against the OMOP CDM and included 8008 entities, 3550 attributes, and 3529 relations. A sequence labeling-based method was developed for automatic entity and attribute recognition. Negation detection was supported by NegEx and a set of predefined rules. Relation extraction was achieved by a support vector machine classifier. We further performed terminology-based concept normalization and output structuring.
resultsIn task-specific evaluations, the best F1 score for entity recognition was 0.79, and for relation extraction was 0.89. The accuracy of negation detection was 0.94. The overall accuracy for query formalization was 0.71 in an end-to-end evaluation.
conclusionsThis study presents EliIE, an OMOP CDM-based information extraction system for automatic structuring and formalization of free-text EC. According to our evaluation, machine learning-based EliIE outperforms existing systems and shows promise to improve.
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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.