Computational Pharmacovigilance for Herbal and Natural Product Medicines: Signal Detection, Interaction Mapping, Adverse Event Interpretation, and Risk Governance

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Herbal and natural product medicines create distinctive pharmacovigilance challenges because safety reports may involve heterogeneous products, uncertain botanical identity, variable preparations, incomplete exposure documentation, co-administered medicines, and patient-reported narratives with missing clinical context. This article proposes an original computational pharmacovigilance framework for safer surveillance of herbal medicines, botanical products, phytochemicals, and natural product preparations. The framework integrates product and exposure identity review, adverse event coding, reporting-quality assessment, duplicate detection, missing-data review, disproportionality screening where supported, text mining, interaction mapping, mechanistic plausibility assessment, causality uncertainty, expert review, risk prioritization, and governance actionability. Its central contribution is to distinguish statistical safety signals from clinical suspicion, mechanistic plausibility, reporting bias, product uncertainty, herb–drug interaction risk, adverse event interpretation, and governance decisions. The proposed model treats computational methods as tools for signal prioritization and evidence organization rather than as independent arbiters of causality, incidence, patient-specific risk, or regulatory action. By linking signal detection to product identity, exposure context, reporting quality, patient vulnerability, co-medication review, causality assessment, uncertainty communication, and feedback loops, the framework supports a more cautious and transparent approach to herbal and natural product medicine safety surveillance.