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

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 p

SUBMITTER: Chang CY 

PROVIDER: S-EPMC10785912 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature