ArticlePatterns (New York, N.Y.)2024
FRAMM: Fair ranking with missing modalities for clinical trial site selection.
Article in Patterns (New York, N.Y.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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
8 citing papers in PubMed, 11 citations in OpenAlex.
- From Invisible to Enrolled: How Artificial Intelligence Is Reshaping Clinical Trial Recruitment.Pharmaceutical medicine · 2026Article
- Design of a card-based operational framework for the implementation of inclusive representation in clinical trials.Trials · 2026Article
- AI/ML-based strategies for enhancing equity, diversity, and inclusion in randomized clinical trials.Trials · 2026Article
- Artificial intelligence in clinical trial participant recruitment and retention: A scoping review and meta-analysis.Journal of clinical and translational science · 2026Review
- Large language models for drug discovery and development.Patterns (New York, N.Y.) · 2025Review
- A scoping review of artificial intelligence applications in clinical trial risk assessment.NPJ digital medicine · 2025Article
- Computational strategic recruitment for representation and coverage studied in the All of Us Research Program.NPJ digital medicine · 2025Article
- Improving medical machine learning models with generative balancing for equity and excellence.NPJ digital medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The underrepresentation of gender, racial, and ethnic minorities in clinical trials is a problem undermining the efficacy of treatments on minorities and preventing precise estimates of the effects within these subgroups. We propose FRAMM, a deep reinforcement learning framework for fair trial site selection to help address this problem. We focus on two real-world challenges: the data modalities used to guide selection are often incomplete for many potential trial sites, and the site selection needs to simultaneously optimize for both enrollment and diversity. To address the missing data challenge, FRAMM has a modality encoder with a masked cross-attention mechanism for bypassing missing data. To make efficient trade-offs, FRAMM uses deep reinforcement learning with a reward function designed to simultaneously optimize for both enrollment and fairness. We evaluate FRAMM using real-world historical clinical trials and show that it outperforms the leading baseline in enrollment-only settings while also greatly improving diversity.
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.