  Clinical trials are indispensable in developing new treatments, but they face
obstacles in patient recruitment and retention, hindering the enrollment of
necessary participants. To tackle these challenges, deep learning frameworks
have been created to match patients to trials. These frameworks calculate the
similarity between patients and clinical trial eligibility criteria,
considering the discrepancy between inclusion and exclusion criteria. Recent
studies have shown that these frameworks outperform earlier approaches.
However, deep learning models may raise fairness issues in patient-trial
matching when certain sensitive groups of individuals are underrepresented in
clinical trials, leading to incomplete or inaccurate data and potential harm.
To tackle the issue of fairness, this work proposes a fair patient-trial
matching framework by generating a patient-criterion level fairness constraint.
The proposed framework considers the inconsistency between the embedding of
inclusion and exclusion criteria among patients of different sensitive groups.
The experimental results on real-world patient-trial and patient-criterion
matching tasks demonstrate that the proposed framework can successfully
alleviate the predictions that tend to be biased.
