Machine Learning Approach for Active Vaccine Safety Monitoring
10.3346/jkms.2021.36.e198
- Author:
Yujeong KIM
1
;
Jong-Hwan JANG
;
Namgi PARK
;
Na-Young JEONG
;
Eunsun LIM
;
Soyun KIM
;
Nam-Kyong CHOI
;
Dukyong YOON
Author Information
1. Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Yongin, Korea
- Publication Type:Original Article
- From:Journal of Korean Medical Science
2021;36(31):e198-
- CountryRepublic of Korea
- Language:English
-
Abstract:
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.
