利用机器学习进行临床试验中给药错误风险的早期风险分层
Early Risk Stratification of Dosing Errors in Clinical Trials Using Machine Learning
AI总结：
本文提出利用机器学习对临床试验中给药错误风险进行早期分层，通过融合结构化和非结构化数据，结合概率校准技术，实现可解释的风险评估。
摘要：
本研究的目的是开发一个基于机器学习（ML）的框架，根据临床试验（CTs）发生高给药错误率的可能性，利用试验启动前可用的信息进行早期风险分层。材料和方法：我们从ClinicalTrials.gov构建了一个包含42,112个CTs的数据集。提取了结构化、半结构化试验数据以及与协议相关的非结构化自由文本数据。CTs被分配二元标签，表示高给药错误率，这些标签来源于不良事件报告、MedDRA术语和Wilson置信区间。我们评估了一个基于结构化特征训练的XGBoost模型、一个使用文本数据的ClinicalModernBERT模型，以及一个结合两种模态的简单晚融合模型。事后概率校准被应用于实现可解释的、试验层面的风险分层。结果：晚融合模型在AUC-ROC上表现最佳（0.862）。除了判别能力外，校准输出使CTs能够被分层到预定义的风险类别中。被标记为具有过高给药错误率比例的试验在更高预测风险组中呈单调增加，并与对应的预测概率范围一致。讨论：这些发现表明，可以利用启动前的信息在试验层面预测给药错误风险。概率校准对于将模型输出转化为可靠且可解释的风险类别至关重要，而简单的多模态整合在不需复杂架构的情况下实现了性能提升。结论：本研究介绍了一种可重复且可扩展的ML框架，用于早期、试验层面的CTs风险分层，这些CTs存在高给药错误率的风险，支持在临床研究中进行主动、基于风险的质量管理。
英文摘要：
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.
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