| Summary: | 碩士 === 國立臺灣大學 === 資訊管理學研究所 === 102 === Adverse Drug Reactions (ADRs) are fatal health problems due to medical treatments. ADRs are leading cause of death, and thus it is crucial to properly monitor post-marketing drugs. However, traditional disproportionality analysis and Bayesian signal detection depend on pre-collected ADR reports and a not universal, predefined threshold; the results are often inconsistent. Moreover, the available data sources were limited to two databases — U.S. FDA’s FAERS and WHO’s VigiBase; there are also several difficulties when detecting ADRs in these databases.
To address above problems, in this study, we proposed a model combining three detecting scores: regression’s t-value (REG), proportional reporting ratio (PRR), and reporting odds ratio (ROR), as features for detecting serious drug-ADR pairs from one-week aggregated patient-week information with precedence relationship between drugs and diagnoses, in an health insurance claims database NHIRD (National Health Insurance Research Database). We demonstrated that the proposed combined score led to an improvement (up to 9.5%) of signal detection accuracy over applying each of score independently.
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