| アブストラクト | Vaccine safety is a critical issue for public health, which has recently become more crucial than ever since COVID-19 started to spread worldwide in 2020. Many COVID-19 vaccines have been developed and used without following the traditional three clinical trial stages. Instead, most COVID-19 vaccines were approved through emergency use approval (EUA) within one year, significantly raising the risk of rare and severe adverse events. Reporting systems like the Vaccine Adverse Event Reporting System (VAERS) have been established worldwide to detect unknown and severe adverse reactions as early as possible. Although experts and researchers have been working hard to find ways to detect adverse vaccine event (AVE) signals from VAERS data, most of the contemporary methods are statistical methods based on measuring the disproportionality between vaccine-induced events and non-vaccine-induced events. This paper proposes a novel ensemble AVE detection method, which adopts a stacking ensemble of various disproportionality indicators, fusing dual-scale contingency values measured in single and cumulative yearly duration, and embraces the concept of feature concatenation. Experiments conducted on US VAERS data to predict AVE caused by COVID-19 vaccines show that our proposed method is effective. We observed that: (1) Stacking ensemble of various disproportionality indicators is superior to any single disproportionality indicator and voting ensemble method; (2) Fusing dual-scale contingency values and feature concatenation brings synergy to our proposed stacking ensemble AVE detection. Compared to the best disproportionality metric in this study, our top-performing ensemble version exhibited a 34% improvement in accuracy, 71% in precision, 29% in recall, and 77% in F-measure, with a slight decrease (8%) in specificity. |
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