[2603.29041] A Latent Risk-Aware Machine Learning Approach for Predicting Operational Success in Clinical Trials based on TrialsBank

Computer Science > Machine Learning arXiv:2603.29041 (cs) [Submitted on 30 Mar 2026] Title:A Latent Risk-Aware Machine Learning Approach for Predicting Operational Success in Clinical Trials based on TrialsBank Authors:Iness Halimi, Emmanuel Piffo, Oumnia Boudersa, Yvan Marcel Carre Vilmorin, Melissa Ait-ikhlef, Karima Kone, Andy Tan, Augustin Medina, Juliette Hernando, Sheila Ernest, Vatche Bartekian, Karine Lalonde, Mireille E Schnitzer, Gianolli Dorcelus View a PDF of the paper titled A Latent Risk-Aware Machine Learning Approach for Predicting Operational Success in Clinical Trials based on TrialsBank, by Iness Halimi and 13 other authors View PDF Abstract:Clinical trials are characterized by high costs, extended timelines, and substantial operational risk, yet reliable prospective methods for predicting trial success before initiation remain limited. Existing artificial intelligence approaches often focus on isolated metrics or specific development stages and frequently rely on variables unavailable at the trial design phase, limiting real-world applicability. We present a hierarchical latent risk-aware machine learning framework for prospective prediction of clinical trial operational success using a curated subset of TrialsBank, a proprietary AI-ready database developed by Sorintellis, comprising 13,700 trials. Operational success was defined as the ability to initiate, conduct, and complete a clinical trial according to planned timelines, recruitment targets, and p...