Users can explore the relationships between various drug classes, individual drugs, and adverse events through interactive network visualizations. The application allows users to select different views, such as drug classes, individual drugs, or specific adverse events, enabling focused analysis tailored to their research interests. By leveraging this application, researchers, healthcare professionals, and pharmaceutical companies can gain insights into potential drug safety issues, identify patterns, and make informed decisions to improve patient outcomes in IBD treatment.

See owner's GitHub repository for more information: https://github.com/balubhasuran/IBD_ADE

Silverman, A. L., Sushil, M., Bhasuran, B., Ludwig, D., Buchanan, J., Racz, R., Parakala, M., El-Kamary, S., Ahima, O., Belov, A., Choi, L., Billings, M., Li, Y., Habal, N., Liu, Q., Tiwari, J., Butte, A. J., & Rudrapatna, V. A. (2023). Algorithmic identification of treatment-emergent adverse events from clinical notes using large language models: a pilot study in inflammatory bowel disease. medRxiv, 2023.09.06.23295149. https://doi.org/10.1101/2023.09.06.23295149

This application was not uploaded by the author, but through their publicly available Github repository, https://github.com/balubhasuran/IBD_ADE.