ArticleJournal of the American Medical Informatics Association : JAMIA2019
Optimizing clinical trials recruitment via deep learning.
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 16 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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, 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
- From Invisible to Enrolled: How Artificial Intelligence Is Reshaping Clinical Trial Recruitment.Pharmaceutical medicine · 2026Article
- Multi-omics and artificial intelligence for precision drug discovery and potential clinical applications.Signal transduction and targeted therapy · 2026Review
- Artificial intelligence in clinical trial participant recruitment and retention: A scoping review and meta-analysis.Journal of clinical and translational science · 2026Review
- Role and Potential of Artificial Intelligence in Biomarker Discovery and Development of Treatment Strategies for Amyotrophic Lateral Sclerosis.International journal of molecular sciences · 2025Review
- Role of AI in empowering and redefining the oncology care landscape: perspective from a developing nation.Frontiers in digital health · 2025Review
- AI in Clinical Trials and Drug Development: Challenges and Potential Advancements.Current drug discovery technologies · 2025Review
- Artificial intelligence for optimizing recruitment and retention in clinical trials: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- FRAMM: Fair ranking with missing modalities for clinical trial site selection.Patterns (New York, N.Y.) · 2024Article
- Enhancing site selection strategies in clinical trial recruitment using real-world data modeling.PloS one · 2024Article
- Artificial Intelligence Applied to clinical trials: opportunities and challenges.Health and technology · 2023Review
- ELaPro, a LOINC-mapped core dataset for top laboratory procedures of eligibility screening for clinical trials.BMC medical research methodology · 2022Article
- Artificial intelligence for assisting cancer diagnosis and treatment in the era of precision medicine.Cancer communications (London, England) · 2021Review
- Crowdsourcing and open innovation in drug discovery: recent contributions and future directions.Drug discovery today · 2020Review
- Evaluation of patient-level retrieval from electronic health record data for a cohort discovery task.JAMIA open · 2020Article
- New approaches to cohort selection.Journal of the American Medical Informatics Association : JAMIA · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveClinical trials, prospective research studies on human participants carried out by a distributed team of clinical investigators, play a crucial role in the development of new treatments in health care. This is a complex and expensive process where investigators aim to enroll volunteers with predetermined characteristics, administer treatment(s), and collect safety and efficacy data. Therefore, choosing top-enrolling investigators is essential for efficient clinical trial execution and is 1 of the primary drivers of drug development cost. MATERIALS AND
methodsTo facilitate clinical trials optimization, we propose DeepMatch (DM), a novel approach that builds on top of advances in deep learning. DM is designed to learn from both investigator and trial-related heterogeneous data sources and rank investigators based on their expected enrollment performance on new clinical trials.
resultsLarge-scale evaluation conducted on 2618 studies provides evidence that the proposed ranking-based framework improves the current state-of-the-art by up to 19% on ranking investigators and up to 10% on detecting top/bottom performers when recruiting investigators for new clinical trials. DISCUSSION: The extensive experimental section suggests that DM can provide substantial improvement over current industry standards in several regards: (1) the enrollment potential of the investigator list, (2) the time it takes to generate the list, and (3) data-informed decisions about new investigators.
conclusionDue to the great significance of the problem at hand, related research efforts are set to shift the paradigm of how investigators are chosen for clinical trials, thereby optimizing and automating them and reducing the cost of new therapies.
Indexed as
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.