用临床试验注册信息预测严重不良事件，帮研究设计更安全。
A novel language model for predicting serious adverse event results in clinical trials from their prospective registrations
- 用预训练语言模型提取注册文本特征，结合滑动窗口处理长文本。
- 预测哪组不良事件更多，准确率AUC达77.6%；预测发生比例，误差RMSE为18.6%。
- 适合临床研究设计者、药企安全评估团队参考，提升试验安全性预判。
目标：准确估计预期安全结果可优化临床试验设计与监测。我们评估了仅基于试验注册信息预测临床试验中严重不良事件（SAE）结果的方法。分析了来自ClinicalTrials.gov的22,107个双臂平行干预试验，包含结构化摘要结果。构建了两类模型：分类器预测实验组相比对照组是否有更高比例的SAE（AUC），回归模型预测对照组中SAE的比例（RMSE）。采用预训练语言模型（如ClinicalT5、BioBERT）进行特征提取，结合下游模型预测，并提出滑动窗口方法以在超长文本下保持语义表示。结果：最佳模型（ClinicalT5+Transformer+MLP）在判断哪组SAE比例更高时达到77.6% AUC；预测对照组SAE比例时RMSE为18.6%。滑动窗口方法始终优于直接比较。12个分类器平均AUC提升2.00%，12个回归器平均RMSE降低1.58%。讨论：ClinicalTrials.gov的摘要数据仍被低估。利用公开报告试验的预测结果，有助于发现预期与实际安全结果间的差异。
原文摘要 · Abstract (English)
Objectives: With accurate estimates of expected safety results, clinical trials could be better designed and monitored. We evaluated methods for predicting serious adverse event (SAE) results in clinical trials using information only from their registrations prior to the trial. Material and Methods: We analyzed 22,107 two-arm parallel interventional clinical trials from ClinicalTrials.gov with structured summary results. Two prediction models were developed: a classifier predicting whether a greater proportion of participants in an experimental arm would have SAEs (area under the receiver operating characteristic curve; AUC) compared to the control arm, and a regression model to predict the proportion of participants with SAEs in the control arms (root mean squared error; RMSE). A transfer learning approach using pretrained language models (e.g., ClinicalT5, BioBERT) was used for feature extraction, combined with a downstream model for prediction. To maintain semantic representation in long trial texts exceeding localized language model input limits, a sliding window method was developed for embedding extraction. Results: The best model (ClinicalT5+Transformer+MLP) had 77.6% AUC when predicting which trial arm had a higher proportion of SAEs. When predicting SAE proportion in the control arm, the same model achieved RMSE of 18.6%. The sliding window approach consistently outperformed direct comparisons. Across 12 classifiers, the average absolute AUC increase was 2.00%, and absolute RMSE reduction was 1.58% across 12 regressors. Discussion: Summary results data from ClinicalTrials.gov remains underutilized. Predicted results of publicly reported trials provides an opportunity to identify discrepancies between expected and reported safety results.
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