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.

翻译：临床试验在开发新疗法中不可或缺，但患者招募和留存环节的障碍制约了必要参与者的入组。为应对这些挑战，研究者构建了深度学习框架来实现患者与试验的匹配。这些框架通过计算患者与临床试验入排标准间的相似度，特别关注纳入与排除标准之间的差异。研究表明，此类框架优于早期方法。然而，当某些敏感群体在临床试验中代表性不足时，深度学习模型可能在患者-试验匹配中引发公平性问题，导致数据不完整或失真，并可能造成危害。为解决公平性问题，本文通过生成患者-标准层级的公平性约束，提出了一种公平的患者-临床试验匹配框架。该框架考虑了不同敏感群体患者中纳入标准与排除标准嵌入表示的不一致性。在真实患者-试验匹配及患者-标准匹配任务上的实验结果表明，该框架能有效缓解预测中的偏见倾向。