Enhancing Patient Recruitment Response in Clinical Trials: an Adaptive Learning Framework

Xinying Fang, Shouhao Zhou

Keywords: Adaptive learning; Patient recruitment; Ensemble learning; Machine Learning; Clinical trial

TL;DR: We develop an adaptive learning framework with machine learning to enhance patient recruitment response in clinical trials.

Abstract: Patient recruitment remains a key challenge in contemporary clinical trials, often leading to trial failures due to insufficient recruitment rates. To address this issue, we introduce a novel adaptive learning framework that integrates machine learning methods to facilitate evidence-informed recruitment. Through dynamic testing, predictive learning, and adaptive pruning of recruitment plans, the proposed framework ensures superiority over the conventional random assignment approach. We discuss the practical considerations for implementing this framework and conduct a simulation study to assess the overall response rates and chances of improvement. The findings suggest that the proposed approach can substantially enhance patient recruitment efficiency. By systematically optimizing recruitment plan allocation, this adaptive learning framework shows promise in addressing recruitment challenges across broad clinical research settings, potentially transforming how patient recruitment is managed in clinical trials.

List Of Authors: Fang, Xinying and Zhou, Shouhao

Code Url: https://github.com/vivid225/Integrating-Machine-Learning-Methods-in-Clinical-Trial-Recruitment

Submission Number: 656