Despite many efforts to address the disparities, the underrepresentation of
gender, racial, and ethnic minorities in clinical trials remains a problem and
undermines the efficacy of treatments on minorities. This paper focuses on the
trial site selection task and proposes FRAMM, a deep reinforcement learning
framework for fair trial site selection. We focus on addressing two real-world
challenges that affect fair trial sites selection: the data modalities are
often not complete for many potential trial sites, and the site selection needs
to simultaneously optimize for both enrollment and diversity since the problem
is necessarily a trade-off between the two with the only possible way to
increase diversity post-selection being through limiting enrollment via caps.
To address the missing data challenge, FRAMM has a modality encoder with a
masked cross-attention mechanism for handling missing data, bypassing data
imputation and the need for complete data in training. To handle the need for
making efficient trade-offs, FRAMM uses deep reinforcement learning with a
specifically designed reward function that simultaneously optimizes for both
enrollment and fairness.
We evaluate FRAMM using 4,392 real-world clinical trials ranging from 2016 to
2021 and show that FRAMM outperforms the leading baseline in enrollment-only
settings while also achieving large gains in diversity. Specifically, it is
able to produce a 9% improvement in diversity with similar enrollment levels
over the leading baselines. That improved diversity is further manifested in
achieving up to a 14% increase in Hispanic enrollment, 27% increase in Black
enrollment, and 60% increase in Asian enrollment compared to selecting sites
with an enrollment-only model.

采用深度强化学习框架 FRAMM 进行公正的试验地点选择，以同时优化招募和多样性并解决数据不完整性问题。在 4,392 个现实的临床试验中评估 FRAMM，并表明其在提高多样性、特别是拉丁裔、黑人和亚洲人的招募方面表现优于基线方法。

FRAMM: 临床试验选址中考虑缺失数据的公平排名

FRAMM: Fair Ranking with Missing Modalities for Clinical Trial Site  Selection

Generative Adversarial Networks (GANs) have brought about rapid progress
towards generating photorealistic images. Yet the equitable allocation of their
modeling capacity among subgroups has received less attention, which could lead
to potential biases against underrepresented minorities if left uncontrolled.
In this work, we first formalize the problem of minority inclusion as one of
data coverage, and then propose to improve data coverage by harmonizing
adversarial training with reconstructive generation. The experiments show that
our method outperforms the existing state-of-the-art methods in terms of data
coverage on both seen and unseen data. We develop an extension that allows
explicit control over the minority subgroups that the model should ensure to
include, and validate its effectiveness at little compromise from the overall
performance on the entire dataset. Code, models, and supplemental videos are
available at GitHub.

通过协调对抗训练和重构生成来提高数据覆盖率，并扩展了一个允许显式控制模型应该包含的少数群体的方法。