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

arXiv cs.AI / 4/1/2026

💬 OpinionSignals & Early TrendsIdeas & Deep AnalysisModels & Research

共有:

Key Points

The paper proposes a hierarchical, latent risk-aware machine learning framework to prospectively predict whether clinical trials will achieve operational success before they begin.
Operational success is defined as initiating, conducting, and completing trials within planned timelines, recruitment targets, and protocol specifications by database lock.
The method uses a staged approach: it first predicts intermediate latent operational risk factors from 180+ drug- and trial-level features available at design time, then uses these latent risks to estimate operational success probability.
Using a curated subset of TrialsBank (13,700 trials), the authors benchmark XGBoost, CatBoost, and Explainable Boosting Machines, reporting strong out-of-sample outcomes across Phase I–III (F1-scores ~0.91–0.93).
The authors report that incorporating latent risk drivers improves discrimination of operational failures and that results remain robust under independent inference evaluation, supporting early risk assessment for data-driven trial planning.

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 protocol specifications through database lock. This approach decomposes operational success prediction into two modeling stages. First, intermediate latent operational risk factors are predicted using more than 180 drug- and trial-level features available before trial initiation. These predicted latent risks are then integrated into a downstream model to estimate the probability of operational success. A staged data-splitting strategy was employed to prevent information leakage, and models were benchmarked using XGBoost, CatBoost, and Explainable Boosting Machines. Across Phase I-III, the framework achieves strong out-of-sample performance, with F1-scores of 0.93, 0.92, and 0.91, respectively. Incorporating latent risk drivers improves discrimination of operational failures, and performance remains robust under independent inference evaluation. These results demonstrate that clinical trial operational success can be prospectively forecasted using a latent risk-aware AI framework, enabling early risk assessment and supporting data-driven clinical development decision-making.