ArticleACR open rheumatology2021
Improving the Efficiency of Clinical Trial Recruitment Using an Ensemble Machine Learning to Assist With Eligibility Screening.
Article in ACR open rheumatology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Improving Participant Recruitment in Clinical Trials: Comparative Analysis of Innovative Digital Platforms.Journal of medical Internet research · 2024Pooled it
- CMC-ID: identifying children and youth with medical complexity using an electronic health record.BMJ health & care informatics · 2026Article
- Blockchain technology for secure and efficient nephrology data management.Clinical kidney journal · 2026Review
- Natural language processing to enhance rheumatoid arthritis care in clinical studies: a scoping review of applications, data, approaches, challenges and future directions.Rheumatology international · 2026Article
- An operational target trial emulation framework for causal inference using electronic health record data.NPJ digital medicine · 2026Review
- 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
- Increasing clinical recruitment rate to a single-site observational study: a quality improvement study.BMJ open quality · 2025Article
- Artificial intelligence as an assistant for studying treatment response in rheumatoid arthritis.Seminars in arthritis and rheumatism · 2025Article
- ImpACT Project: Improving Access to Clinical Trials in Victoria, an Artificial Intelligence-Based Approach.JCO clinical cancer informatics · 2025Article
- Artificial intelligence for optimizing recruitment and retention in clinical trials: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Applying Artificial Intelligence in Pediatric Clinical Trials: Potential Impacts and Obstacles.The journal of pediatric pharmacology and therapeutics : JPPT : the official journal of PPAG · 2024Article
- Article
- Predicting Phase 1 Lymphoma Clinical Trial Durations Using Machine Learning: An In-Depth Analysis and Broad Application Insights.Clinics and practice · 2023Article
- Machine learning approaches for electronic health records phenotyping: a methodical review.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Text Classification of Cancer Clinical Trial Eligibility Criteria.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- Clinical trial recruitment in primary care: exploratory factor analysis of a questionnaire to measure barriers and facilitators to primary care providers' involvement.BMC primary care · 2022Article
- Artificial Intelligence in Rheumatoid Arthritis: Current Status and Future Perspectives: A State-of-the-Art Review.Rheumatology and therapy · 2022Review
- ELaPro, a LOINC-mapped core dataset for top laboratory procedures of eligibility screening for clinical trials.BMC medical research methodology · 2022Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
objectiveEfficiently identifying eligible patients is a crucial first step for a successful clinical trial. The objective of this study was to test whether an approach using electronic health record (EHR) data and an ensemble machine learning algorithm incorporating billing codes and data from clinical notes processed by natural language processing (NLP) can improve the efficiency of eligibility screening.
methodsWe studied patients screened for a clinical trial of rheumatoid arthritis (RA) with one or more International Classification of Diseases (ICD) code for RA and age greater than 35 years, from a tertiary care center and a community hospital. The following three groups of EHR features were considered for the algorithm: 1) structured features, 2) the counts of NLP concepts from notes, 3) health care utilization. All features were linked to dates. We applied random forest and logistic regression with least absolute shrinkage and selection operator penalty against the following two standard approaches: 1) one or more RA ICD code and no ICD codes related to exclusion criteria (Screen
resultsIn total, 3359 patients at Brigham and Women's Hospital (BWH) and 642 patients at Faulkner Hospital (FH) were studied, with 461 (13.7%) eligible patients at BWH and 84 (13.4%) at FH. The application of the algorithm reduced ineligible patients from chart review by 40.5% at the tertiary care center and by 57.0% at the community hospital. In contrast, Screen
conclusionThe ensemble machine learning algorithm incorporating billing codes and NLP data increased the efficiency of eligibility screening by reducing the number of patients requiring chart review while not excluding eligible patients. Moreover, this approach can be trained at one institution and applied at another for multicenter clinical trials.
Identifiers
What 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.