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Artificial intelligence models predicting abnormal uterine bleeding after COVID-19 vaccination.

Yunjeong Choi1, Jaeyu Park2, Hyejun Kim2,3

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A machine learning model can predict abnormal uterine bleeding (AUB) after COVID-19 vaccination in women under 50. Key predictors include vaccination frequency, Novavax vaccine count, and hemoglobin levels, aiding risk assessment.

Keywords:
Abnormal uterine bleedingCOVID-19 vaccinationEnsemble modelsMachine learning

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Area of Science:

  • Vaccinology
  • Data Science
  • Reproductive Health

Background:

  • COVID-19 vaccine surveillance identified abnormal uterine bleeding (AUB) as a concern.
  • Predictive models for post-vaccination AUB are needed for risk assessment.

Purpose of the Study:

  • Develop a machine learning (ML) model to predict AUB in women under 50 after COVID-19 vaccination.
  • Identify key predictive factors for post-vaccination AUB.

Main Methods:

  • Utilized the Korean Nationwide Cohort (K-COV-N) of over 7 million participants.
  • Employed ensemble ML models (gradient boosting machine, logistic regression) with Synthetic Minority Over-sampling Technique (SMOTE) for data balancing.
  • Conducted feature importance analysis on a cohort of over 2 million vaccinated individuals.

Main Results:

  • Developed the first ML model to predict post-vaccination AUB.
  • Identified COVID-19 vaccination frequency, NVX-CoV2373 (Novavax) vaccination count, and hemoglobin levels as primary predictive features.
  • Feature importance analysis provided insights into risk factors.

Conclusions:

  • The developed ML model offers a novel approach to predicting AUB risk following COVID-19 vaccination.
  • Findings can inform enhanced post-vaccination monitoring strategies and risk stratification.
  • Highlights the importance of vaccination history and physiological factors in predicting AUB.