FRAMM: Fair ranking with missing modalities for clinical trial site selection
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View abstract on PubMed
Summary
This summary is machine-generated.Clinical trial site selection is improved by FRAMM, a new deep reinforcement learning framework. FRAMM enhances participant diversity and enrollment by addressing incomplete data and optimizing for both goals simultaneously.
Area Of Science
- Biomedical Informatics
- Clinical Trial Management
- Artificial Intelligence in Healthcare
Background
- Underrepresentation of diverse populations in clinical trials impacts treatment efficacy and subgroup analysis.
- Existing clinical trial site selection methods struggle with incomplete data and balancing enrollment with diversity.
Purpose Of The Study
- To introduce FRAMM, a deep reinforcement learning framework for fair clinical trial site selection.
- To address challenges of incomplete data modalities and simultaneous optimization of enrollment and diversity.
Main Methods
- FRAMM utilizes a modality encoder with masked cross-attention to handle missing data.
- A deep reinforcement learning approach with a custom reward function optimizes for both enrollment and fairness.
- Evaluation performed using real-world historical clinical trial data.
Main Results
- FRAMM outperforms leading baselines in enrollment-only scenarios.
- The framework significantly improves participant diversity in clinical trials.
- Demonstrates effective trade-offs between enrollment numbers and demographic representation.
Conclusions
- FRAMM offers a novel solution for fair and efficient clinical trial site selection.
- The framework effectively addresses data limitations and optimizes for multiple objectives.
- FRAMM has the potential to improve the generalizability and precision of clinical trial findings across diverse populations.
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