A novel model using ML techniques for clinical trial design and expedited patient onboarding process

Abstract

In this paper, we present novel research that leverages machine learning (ML) models and techniques to automate the outcome prediction of clinical trials. Our study is motivated to combine two crucial aspects, namely, the streamlined selection process of the site of action for a new drug and the optimization of patient enrolment in clinical trials. This unique combination provides an end-to-end solution to proceed with Phase 1 of clinical trials, effectively addressing the limitations that can impede the success of the trial process. By improving the target site selection process, the probability of successful completion of clinical trials increases with minimum system time and spent resources of pharmaceutical companies and researchers, in addition to ensuring the improved safety of patients enrolled in the trials. The model presented in this paper not only enhances the site selection process but also aims to streamline the patient enrolment process, directly targeting the challenges associated with low accrual rates and enrolment inefficiency reported in global statistical analyses of terminated trials within clinical trials databases.2 The empirical results derived from our model are presented, demonstrating its efficacy in addressing these critical issues and providing a comprehensive solution for enhancing the efficiency and success rates of clinical trials. To establish a robust test bed, we collected and analysed data from 273,254 terminated or completed studies obtained from the ClinicalTrials.gov site. This dataset served as the foundation for constructing a test bed of 55,000 samples, encompassing trials conducted across nations such as Australia, Canada, India, France, USA, UK, and Switzerland, among others. Employing feature engineering, ensemble learning, and the tf-idf technique, we achieved a balanced accuracy score of 71% and an Area Under the Curve (AUC) of 0.70, in determining the outcomes of a clinical trial, which further enhances the site of action selection process. Finally, to streamline patient enrolment, we acquired a dataset consisting of information from 600 patients, focusing specifically on liver disease conditions. Within this context, we employed ensemble learning, feature selection, and artificial neural networks to develop an algorithm to assess patient eligibility for clinical trials targeting exclusively on liver-related ailments. Bespoke eligibility criteria were incorporated in the algorithm, enabling an efficient eligibility determination of patient records. Our results are impressive, if not promising, with a 73% test accuracy, in showcasing its potential for automating and optimizing the patient enrolment process in clinical trials.