Towards Fair Patient-Trial Matching via Patient-Criterion Level Fairness Constraint

• URL: http://arxiv.org/abs/2303.13790v1
• Date: Fri, 24 Mar 2023 03:59:19 GMT
• Title: Towards Fair Patient-Trial Matching via Patient-Criterion Level Fairness Constraint
• Authors: Chia-Yuan Chang, Jiayi Yuan, Sirui Ding, Qiaoyu Tan, Kai Zhang, Xiaoqian Jiang, Xia Hu, Na Zou
• Abstract summary: This work proposes a fair patient-trial matching framework by generating a patient-criterion level fairness constraint. 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.
• Score: 50.35075018041199
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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