Research Paper#Adverse Drug Reaction Prediction, Federated Learning, Transformer🔬 ResearchAnalyzed: Jan 3, 2026 16:09
Federated Learning for Adverse Drug Reaction Prediction
Published:Dec 29, 2025 07:42
•1 min read
•ArXiv
Analysis
This paper addresses the problem of biased data in adverse drug reaction (ADR) prediction, a critical issue in healthcare. The authors propose a federated learning approach, PFed-Signal, to mitigate the impact of biased data in the FAERS database. The use of Euclidean distance for biased data identification and a Transformer-based model for prediction are novel aspects. The paper's significance lies in its potential to improve the accuracy of ADR prediction, leading to better patient safety and more reliable diagnoses.
Key Takeaways
Reference
“The accuracy rate, F1 score, recall rate and AUC of PFed-Signal are 0.887, 0.890, 0.913 and 0.957 respectively, which are higher than the baselines.”