Machine Learning Approach for Active Vaccine Safety Monitoring.

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 case-crossover design based machine learning model to predict the health outcome of interest events (anaphylaxis and agranulocytosis) using a random

SUBMITTER: Kim Y 

PROVIDER: S-EPMC8352788 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature