Early Risk Stratification of Dosing Errors in Clinical Trials Using Machine Learning

2026-02-25

AI Summary

This study addresses the frequent occurrence of medication errors in clinical trials by proposing an interpretable risk stratification framework to identify high-risk protocols prior to trial initiation, enabling proactive quality control. For the first time, the approach integrates structured pre-startup data and unstructured protocol text through a multimodal architecture: XGBoost models structured features, while ClinicalModernBERT processes free-text content. Multimodal fusion is achieved via late fusion combined with post-hoc probability calibration. The resulting model achieves an AUC-ROC of 0.862, and the calibrated risk categories demonstrate strong alignment with actual high error rates, validating the efficacy of this straightforward yet effective multimodal integration strategy. This framework provides a reliable foundation for early intervention in clinical trial management.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Computer Vision: Multi-modal Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Objective: The objective of this study is to develop a machine learning (ML)-based framework for early risk stratification of clinical trials (CTs) according to their likelihood of exhibiting a high rate of dosing errors, using information available prior to trial initiation. Materials and Methods: We constructed a dataset from ClinicalTrials.gov comprising 42,112 CTs. Structured, semi-structured trial data, and unstructured protocol-related free-text data were extracted. CTs were assigned binary labels indicating elevated dosing error rate, derived from adverse event reports, MedDRA terminology, and Wilson confidence intervals. We evaluated an XGBoost model trained on structured features, a ClinicalModernBERT model using textual data, and a simple late-fusion model combining both modalities. Post-hoc probability calibration was applied to enable interpretable, trial-level risk stratification. Results: The late-fusion model achieved the highest AUC-ROC (0.862). Beyond discrimination, calibrated outputs enabled robust stratification of CTs into predefined risk categories. The proportion of trials labeled as having an excessively high dosing error rate increased monotonically across higher predicted risk groups and aligned with the corresponding predicted probability ranges. Discussion: These findings indicate that dosing error risk can be anticipated at the trial level using pre-initiation information. Probability calibration was essential for translating model outputs into reliable and interpretable risk categories, while simple multimodal integration yielded performance gains without requiring complex architectures. Conclusion: This study introduces a reproducible and scalable ML framework for early, trial-level risk stratification of CTs at risk of high dosing error rates, supporting proactive, risk-based quality management in clinical research.

Problem

• dosing errors
• risk stratification
• clinical trials
• machine learning
• early prediction

Innovation

• risk stratification
• dosing errors
• machine learning
• multimodal fusion
• probability calibration