Masters Theses
Date of Award
5-2023
Degree Type
Thesis
Degree Name
Master of Science
Major
Industrial Engineering
Major Professor
Andrew Yu
Committee Members
John Kobza, James Simonton, Andrew Yu
Abstract
One of the biggest challenges the clinical research industry currently faces is the accurate forecasting of patient enrollment (namely if and when a clinical trial will achieve full enrollment), as the stochastic behavior of enrollment can significantly contribute to delays in the development of new drugs, increases in duration and costs of clinical trials, and the over- or under- estimation of clinical supply. This study proposes a Machine Learning model using a Fully Convolutional Network (FCN) that is trained on a dataset of 100,000 patient enrollment data points including patient age, patient gender, patient disease, investigational product, study phase, blinded vs. unblinded, sponsor CRO selection, enrollment quarter, and enrollment country values to predict patient enrollment characteristics in clinical trials. The model was tested using a dataset consisting of 5,000 data points and yielded a high level of accuracy. This development in patient enrollment prediction will optimize portfolio demand planning and help avoid costs associated with inaccurate patient enrollment forecasting.
Recommended Citation
Shoieb, Ahmed, "A Machine Learning Approach for Predicting Clinical Trial Patient Enrollment in Drug Development Portfolio Demand Planning. " Master's Thesis, University of Tennessee, 2023.
https://trace.tennessee.edu/utk_gradthes/9836
Included in
Artificial Intelligence and Robotics Commons, Clinical Epidemiology Commons, Clinical Trials Commons, Industrial Engineering Commons, Investigative Techniques Commons, Other Medical Sciences Commons, Other Operations Research, Systems Engineering and Industrial Engineering Commons, Pharmacy and Pharmaceutical Sciences Commons, Theory and Algorithms Commons