Publication2025
Deep Learning for Predicting Drug-Drug Interactions and Patient Safety in Dental Clinical Trials
The record
- Type
- Publication
- Date
- 1 January 2025
- Level
- International — held abroad
- Journal
- Proceedings of 2025 10th International Conference on Science Technology Engineering and Mathematics Iconstem 2025
- Published
- 1 January 2025
- DOI
- 10.1109/iconstem65670.2025.11374791
- Scopus ID
- 105034469228
- Authors
- 5
- Open access
- No
- Citations
- 0
- International co-authorship
- Yes
- Co-author countries
- IN, MA, UZ
Classification
Every label below carries the reasoning it was assigned on, the confidence band it was assigned in, and what produced it. The rubric is published.
UN Sustainable Development Goals
3 Ensure healthy lives and promote well-being for all at all ages
Deep Federated Transformer Graph Attention Network for drug-drug interaction prediction achieves 93.8% accuracy, improving patient safety in dental clinical trials through identification of harmful medication interactions.
- High confidence
- The subject matter is a strong and direct inference from the record type, the funding or organising body, or the programme context, even where the title alone is thin. Example: an ICMR-funded project against SDG 3, on the strength of the agency.
- Judged
- Judged against the published classification rubric, under the rubric and prompt versions recorded with the run that produced it.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
9.5 Enhance scientific research, upgrade technological capabilities, and increase researchers and R&D spending
Novel federated learning architecture with graph attention mechanisms demonstrates innovation in pharmacological AI research and privacy-aware clinical decision support systems.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
17.6 Enhance North-South, South-South and triangular cooperation on science, technology and innovation, and knowledge sharing
Structural signal: co-authorship outside India.
- Rule-derived
- Emitted by a deterministic rule from structural signals rather than judged from the record’s text. The rule carries a version, so rule-derived and judged labels stay separable.
- Produced by
- sdg17-structural-v1, under kortex-publication-v1
Viksit Bharat @2047
vb-1 Empowered Indians
Derived through the seeded SDG-to-Viksit-Bharat crosswalk from the primary SDG 3 label. Deep Federated Transformer Graph Attention Network for drug-drug interaction prediction achieves 93.8% accuracy, improving patient safety in dental clinical trials through identification of harmful medication interactions.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Each label above carries the instrument and the version that produced it. How each classification is reached.
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- Source rows resolved into this record
- 1
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Citing this record
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KIIT Deemed to be University. “Deep Learning for Predicting Drug-Drug Interactions and Patient Safety in Dental Clinical Trials”. Kortex.