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논문 기본 정보

자료유형
학술저널
저자정보
Kim Yujeong (Department of Biomedical Systems Informatics Yonsei University College of Medicine Yongin Korea.) Jang Jong-Hwan (Department of Biomedical Systems Informatics Yonsei University College of Medicine Yongin Korea.) Park Namgi (Department of Biomedical Informatics Ajou University School of Medicine Suwon Korea.) Jeong Na-Young (Department of Health Convergence Ewha Womans University Seoul Korea.) Lim Eunsun (Department of Health Convergence Ewha Womans University Seoul Korea.) Kim Soyun (Department of Biomedical Informatics Ajou University School of Medicine Suwon Korea.) Choi Nam-Kyong (Department of Health Convergence Ewha Womans University Seoul Korea.) Yoon Dukyong (Department of Biomedical Systems Informatics Yonsei University College of Medicine Yongin Korea.Cen)
저널정보
대한의학회 Journal of Korean Medical Science Journal of Korean Medical Science Vol.36 No.31
발행연도
2021.1
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1 - 13 (13page)

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Background: Vaccine safety surveillance is important because it is related to vaccine hesitancy, which affects vaccination rate. To increase confidence in vaccination, the active monitoring of vaccine adverse events is important. For effective active surveillance, we developed and verified a machine learning-based active surveillance system using national claim data. Methods: We used two databases, one from the Korea Disease Control and Prevention Agency, which contains flu vaccination records for the elderly, and another from the National Health Insurance Service, which contains the claim data of vaccinated people. We developed a casecrossover design based machine learning model to predict the health outcome of interest events (anaphylaxis and agranulocytosis) using a random forest. Feature importance values were evaluated to determine candidate associations with each outcome. We investigated the relationship of the features to each event via a literature review, comparison with the Side Effect Resource, and using the Local Interpretable Model-agnostic Explanation method. Results: The trained model predicted each health outcome of interest with a high accuracy (approximately 70%). We found literature supporting our results, and most of the important drug-related features were listed in the Side Effect Resource database as inducing the health outcome of interest. For anaphylaxis, flu vaccination ranked high in our feature importance analysis and had a positive association in Local Interpretable Model-Agnostic Explanation analysis. Although the feature importance of vaccination was lower for agranulocytosis, it also had a positive relationship in the Local Interpretable Model-Agnostic Explanation analysis. Conclusion: We developed a machine learning-based active surveillance system for detecting possible factors that can induce adverse events using health claim and vaccination databases. The results of the study demonstrated a potentially useful application of two linked national health record databases. Our model can contribute to the establishment of a system for conducting active surveillance on vaccination.
#Vaccines #Adverse Effects #Postmarketing Product Surveillance #Machine Learning #Cross-over Studies

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참고문헌 (23)

참고문헌 신청
정재훈 / 2021 / Preparing for the Coronavirus Disease (COVID-19) Vaccination: Evidence, Plans, and Implications / Journal of Korean Medical Science 36(7):1~20 dbpia Salmon DA / 2015 / Vaccine hesitancy: causes, consequences, and a call to action / Vaccine 33(Suppl 4):D66~D71 google schola Loomba S / 2021 / Measuring the impact of COVID-19 vaccine misinformation on vaccination intent in the UK and USA / Nat Hum Behav 5(3):337~348 google schola Dubé E / 2013 / Vaccine hesitancy : an overview / Hum Vaccin Immunother 9(8):1763~1773 google schola Takahashi H / 2000 / Adverse events after Japanese encephalitis vaccination : review of post-marketing surveillance data from Japan and the United States / Vaccine 18(26):2963~2969 google schola

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