FRAMM: Fair Ranking with Missing Modalities for Clinical Trial Site
  Selection
Brandon Theodorou Lucas Glass Cao Xiao Jimeng Sun
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.