| アブストラクト | In this study, we utilized ontology and machine learning methods to analyze the current results on vaccine adverse events. With the VAERS (Vaccine Adverse Event Reporting System) Database, the side effects of COVID-19 vaccines are summarized, and a relational/graph database was implemented for further applications and analysis. The adverse effects of COVID-19 vaccines up to March 2022 were utilized in the study. With the built network of the adverse effects of COVID-19 vaccines, the API can help provide a visualized interface for patients, healthcare providers and healthcare officers to quickly find the information of a certain patient and the potential relationships of side effects of a certain vaccine. In the meantime, the model was further applied to predict the key feature symptoms that contribute to hospitalization and treatment following receipt of a COVID-19 vaccine and the performance was evaluated with a confusion matrix method. Overall, our study built a user-friendly visualized interface of the side effects of vaccines and provided insight on potential adverse effects with ontology and machine learning approaches. The interface and methods can be expanded to all FDA (Food and Drug Administration)-approved vaccines. |
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| ジャーナル名 | Healthcare (Basel, Switzerland) |
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| 投稿日 | 2022/8/27 |
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| 投稿者 | Liu, Zhiyuan; Gao, Ximing; Li, Chenyu |
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| 組織名 | Stanford Center for Professional Development, Stanford University, Stanford, CA;94305, USA.;Department of Biomedical Data Science, Stanford University, Stanford, CA 94305,;USA. |
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| Pubmed リンク | https://www.ncbi.nlm.nih.gov/pubmed/36011076/ |
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