Knowledge Graph-Enhanced AI for Early Detection of Adverse Drug Reactions from Longitudinal Clinical Data
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Abstract
Early detection of adverse drug reactions (ADRs) is challenging because drug-related risks may emerge from complex interactions among medications, diseases, laboratory abnormalities, and patient-specific characteristics. This study proposes a knowledge graph-enhanced AI framework for ADR prediction using longitudinal clinical data. A heterogeneous medical graph is constructed with patients, drugs, diagnoses, laboratory abnormalities, and adverse events as interconnected entities. Graph neural networks are used to learn relational representations, while a temporal encoder captures changes in medication exposure and clinical measurements over time. The resulting graph and temporal representations are integrated through a gated fusion mechanism for patient-level ADR risk estimation. Evaluation is performed across common adverse-event categories and compared with conventional machine learning, sequential neural networks, and graph-based baselines. Ablation experiments further quantify the contributions of drug–drug interactions, disease–drug relationships, and temporal laboratory information. The proposed framework improves early identification of high-risk patients while providing relational evidence that helps explain predicted adverse reactions. This approach illustrates how structured biomedical knowledge and longitudinal patient data can be jointly exploited for pharmacovigilance.
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