Automated Detection of Dosing Errors in Clinical Trial Narratives: A Multi-Modal Feature Engineering Approach with LightGBM

This research presents an automated system for detecting dosing errors in clinical trial narratives, leveraging LightGBM with comprehensive multi-modal feature engineering. It combines traditional NLP, semantic embeddings, medical patterns, and transformer scores to achieve high ROC-AUC on an imbalanced dataset.