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Early Risk Stratification of Dosing Errors in Clinical Trials Using Machine Learning
arXiv – CS AI|F\'elicien H\^eche, Sohrab Ferdowsi, Anthony Yazdani, Sara Sansaloni-Pastor, Douglas Teodoro||5 views
🤖AI Summary
Researchers developed a machine learning framework to predict which clinical trials are likely to have high dosing error rates before the trials begin. The system analyzed 42,112 clinical trials and achieved 86.2% accuracy using a combination of structured data and text analysis, enabling proactive risk management in clinical research.
Key Takeaways
- →Machine learning model achieved 86.2% accuracy in predicting clinical trial dosing error risks using pre-trial data.
- →The framework analyzed 42,112 clinical trials from ClinicalTrials.gov combining structured and unstructured text data.
- →Late-fusion model combining XGBoost and ClinicalModernBERT outperformed single-modality approaches.
- →Probability calibration was essential for translating model outputs into reliable risk categories.
- →The framework enables proactive, risk-based quality management in clinical research before trials begin.
#machine-learning#clinical-trials#healthcare-ai#risk-prediction#xgboost#bert#medical-research#data-analysis
Read Original →via arXiv – CS AI
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