aceAI-Powered Scoring System for Detecting Drug-Drug Interaction Insights from Scientific Literature
Background and Objective: Drug-drug interactions (DDIs) present major challenges in clinical care, impacting patient safety and therapeutic outcomes. Traditional detection methods, often based on curated databases, may miss newly emerging interactions or require frequent manual updates. Recent advances in natural language processing (NLP) and artificial intelligence (AI) offer novel tools for real-time, automated DDI detection directly from biomedical literature. This study introduces an AI-powered approach to i…
An NLP pipeline with a weighted relevance score retrieved substantially more relevant drug interaction articles than manually compiled expert reference lists, and did so without manual updates.
Performance was weaker for specific drug-drug queries, with an F1-score of 0.684, than for drug-family queries, and validation relied on a manually curated dataset rather than clinical outcomes.
Evidence
- Peer-reviewedInnovative Insights in Digital Health2026-10-15
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Truvace Impact Record TRV-2026-1610, v1: “AI-Powered Scoring System for Detecting Drug-Drug Interaction Insights from Scientific Literature.” Truvace, 2026-10-16. /record/TRV-2026-1610 (accessed at citation time). sha256 6c2cc2bfded57144…
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