ArticleJournal of the American Medical Informatics Association : JAMIA2019
Hybrid bag of approaches to characterize selection criteria for cohort identification.
Article in Journal of the American Medical Informatics Association : JAMIA, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review on natural language processing systems for eligibility prescreening in clinical research.Journal of the American Medical Informatics Association : JAMIA · 2021Pooled it
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- 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
- Utilizing Large Language Models for Enhanced Clinical Trial Matching: A Study on Automation in Patient Screening.Cureus · 2024Article
- Automated-detection of risky alcohol use prior to surgery using natural language processing.Alcohol, clinical & experimental research · 2024Article
- Artificial Intelligence Applied to clinical trials: opportunities and challenges.Health and technology · 2023Review
- Identifying Caregiver Availability Using Medical Notes With Rule-Based Natural Language Processing: Retrospective Cohort Study.JMIR aging · 2022Article
- Combining human and machine intelligence for clinical trial eligibility querying.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Evaluation of Doc'EDS: a French semantic search tool to query health documents from a clinical data warehouse.BMC medical informatics and decision making · 2022Article
- Artificial Intelligence-Driven Structurization of Diagnostic Information in Free-Text Pathology Reports.Journal of pathology informatics · 2020Article
- New approaches to cohort selection.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- Cohort selection for clinical trials: n2c2 2018 shared task track 1.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- Cohort Selection for Clinical Trials From Longitudinal Patient Records: Text Mining Approach.JMIR medical informatics · 2019Article
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Authors and funding
17 authors.
Funding
Abstract
objectiveThe 2018 National NLP Clinical Challenge (2018 n2c2) focused on the task of cohort selection for clinical trials, where participating systems were tasked with analyzing longitudinal patient records to determine if the patients met or did not meet any of the 13 selection criteria. This article describes our participation in this shared task. MATERIALS AND
methodsWe followed a hybrid approach combining pattern-based, knowledge-intensive, and feature weighting techniques. After preprocessing the notes using publicly available natural language processing tools, we developed individual criterion-specific components that relied on collecting knowledge resources relevant for these criteria and pattern-based and weighting approaches to identify "met" and "not met" cases.
resultsAs part of the 2018 n2c2 challenge, 3 runs were submitted. The overall micro-averaged F1 on the training set was 0.9444. On the test set, the micro-averaged F1 for the 3 submitted runs were 0.9075, 0.9065, and 0.9056. The best run was placed second in the overall challenge and all 3 runs were statistically similar to the top-ranked system. A reimplemented system achieved the best overall F1 of 0.9111 on the test set. DISCUSSION: We highlight the need for a focused resource-intensive effort to address the class imbalance in the cohort selection identification task.
conclusionOur hybrid approach was able to identify all selection criteria with high F1 performance on both training and test sets. Based on our participation in the 2018 n2c2 task, we conclude that there is merit in continuing a focused criterion-specific analysis and developing appropriate knowledge resources to build a quality cohort selection system.
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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.