Machine Learning Approach for Active Vaccine Safety Monitoring
- 주제(키워드) Vaccines , Adverse Effects , Postmarketing Product Surveillance , Machine Learning , Cross-over Studies
- 주제(기타) Medicine, General & Internal
- 설명문(일반) [Kim, Yujeong; Jang, Jong-Hwan; Yoon, Dukyong] Yonsei Univ, Dept Biomed Syst Informat, Coll Med, 363 Dongbaekjukjeon Daero, Yongin 16995, South Korea; [Park, Namgi; Kim, Soyun] Ajou Univ, Dept Biomed Informat, Sch Med, Suwon, South Korea; [Jeong, Na-Young; Lim, Eunsun; Choi, Nam-Kyong] Ewha Womans Univ, Dept Hlth Convergence, Seoul, South Korea; [Yoon, Dukyong] Yonsei Univ Hlth Syst, Yongin Severance Hosp, Ctr Digital Hlth, Yongin, South Korea
- 등재 SCIE, SCOPUS, KCI등재
- 발행기관 KOREAN ACAD MEDICAL SCIENCES
- 발행년도 2021
- URI http://www.dcollection.net/handler/ewha/000000183277
- 본문언어 영어
- Published As http://dx.doi.org/10.3346/jkms.2021.36.e198
초록/요약
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 case-crossover 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.
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