Umme Afroz

Research

My research focuses on detecting adverse drug reactions (ADRs) in post-market pharmacovigilance using natural language processing and large language models.

Specifically, I investigate the following:

- Developing hybrid NLP pipelines that integrate traditional machine learning with LLM-based classifiers to extract adverse reactions from informal sources, comparing them against formal FDA reporting databases.
- Designing and validating statistical approaches with log-scaling and on-label use adjustments to improve signal detection.
- LLM-based screening pipelines.
- Age- and sex-stratified analysis of adverse drug reactions.

Publications

Accepted Deep Learning Approaches for Protein Secondary Structure Prediction. IEEE Xplore. Available: https://ieeexplore.ieee.org/document/11021832

Protein Secondary Structure Prediction Using Attention-Based Fusion of Language Models (pp. 357–372). Springer. Available: https://link.springer.com/chapter/10.1007/978-3-032-15346-3_25