Graph neural network-based subgraph analysis for predicting adverse drug events
The network-based approach demonstrates the potential of GNNs in supporting the early detection and prevention of ADEs. Accurately predicting ADEs could enable healthcare professionals to make informed clinical decisions, take preventive measures and adjust medication regimens before serious adverse events occur. The proposed prediction method could also lead to optimized usage of healthcare resources by preventing hospital admissions and reducing the overall burden of adverse drug events on the healthcare systems.
Authors
- Fangyu Zhou (ORCID: https://orcid.org/0000-0001-8499-9920)
- Matloob Khushi (ORCID: https://orcid.org/0000-0001-7792-2327)
- Shahadat Uddin (ORCID: https://orcid.org/0000-0003-0091-6919)
- Jonathan Brett (ORCID: https://orcid.org/0000-0003-3065-7495)
Institutions
- The University of Sydney (AU)
- St Vincent's Hospital Sydney (AU)
- St Vincent's Clinic (AU)
- Brunel University of London (GB)
Publication Details
- Journal
- Computers in Biology and Medicine
- Published
- 2024-10-23
- DOI
- https://doi.org/10.1016/j.compbiomed.2024.109282
- Citations
- 9
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 3.25
Funders
- UK Research and Innovation
- Natural Environment Research Council