A Novel Model Using ML Techniques for Clinical Trial Design and Expedited Patient Onboarding Process - Iyer A; Narayanaswami S (ClinicoEconomics and Outcomes Research)
Abhirvey Iyer,1 Sundaravalli Narayanaswami2 1Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (BHU) Varanasi, Varanasi, Uttar Pradesh, India; 2Public Systems Group, Indian Institute of Management Ahmedabad, Ahmedabad, Gujarat, IndiaCorrespondence: Sundaravalli Narayanaswami, Email sundaravallin@iima.ac.inIntroduction: Clinical trials are critical for drug development and patient care; however, they often need more efficient trial design and patient enrolment processes. This research explores integrating machine learning (ML) techniques to address these challenges. Specifically, the study investigates ML models for two critical aspects: (1) streamlining clinical trial design parameters (like the site of drug action, type of Interventional/Observational model, etc) and (2) optimizing patient/volunteer enrolment for trials through efficient classification techniques.Methods: The study utilized two datasets: the first, with 55,000 samples (from ClinicalTrials.gov), was divided into five subsets (10,000– 15,000 rows each) for model evaluation, focusing on trial parameter optimization. The second dataset targeted patient eligibility classification (from the UCI ML Repository). Five ML models—XGBoost, Random Forest, Support Vector Classifier (SVC), Logistic Regression, and Decision Tree—were applied to both datasets, alongside Artificial Neural Networks (ANN) for the second dataset. Model performance was evaluated using precision, recall, balanced accuracy, ROC-AUC, and weighted F1-score, with results averaged across k-fold cross-validation.Results: In the first phase, XGBoost and Random Forest emerged as the best-performing models across all five subsets, achieving an average balanced accuracy of 0.71 and an average ROC-AUC of 0.7. The second dataset analysis revealed that while SVC and ANN performed well, ANN was preferred for its scalability to larger datasets. ANN achieved a test accuracy of 0.73714, demonstrating its potential for real-world implementation in patient streamlining.Discussion: The study highlights the effectiveness of ML models in improving clinical trial workflows. XGBoost and Random Forest demonstrated robust performance for large clinical datasets in optimizing trial parameters, while ANN proved advantageous for patient eligibility classification due to its scalability. These findings underscore the potential of ML to enhance decision-making, reduce delays, and improve the accuracy of clinical trial outcomes. As ML technology continues to evolve, its integration into clinical research could drive innovation and improve patient care.Keywords: clinical trials, site selection, machine learning, patient onboarding, feature engineering, feature encoding