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

Xinying Fang, Shouhao Zhou
Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence, PMLR 244:1307-1322, 2024.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v244-fang24b, title = {Enhancing Patient Recruitment Response in Clinical Trials: an Adaptive Learning Framework}, author = {Fang, Xinying and Zhou, Shouhao}, booktitle = {Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence}, pages = {1307--1322}, year = {2024}, editor = {Kiyavash, Negar and Mooij, Joris M.}, volume = {244}, series = {Proceedings of Machine Learning Research}, month = {15--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v244/main/assets/fang24b/fang24b.pdf}, url = {https://proceedings.mlr.press/v244/fang24b.html}, 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.} }
Endnote
%0 Conference Paper %T Enhancing Patient Recruitment Response in Clinical Trials: an Adaptive Learning Framework %A Xinying Fang %A Shouhao Zhou %B Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2024 %E Negar Kiyavash %E Joris M. Mooij %F pmlr-v244-fang24b %I PMLR %P 1307--1322 %U https://proceedings.mlr.press/v244/fang24b.html %V 244 %X 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.
APA
Fang, X. & Zhou, S.. (2024). Enhancing Patient Recruitment Response in Clinical Trials: an Adaptive Learning Framework. Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research 244:1307-1322 Available from https://proceedings.mlr.press/v244/fang24b.html.

Related Material