  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.
