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Predicting Adverse Drug Events in Chinese Pediatric Inpatients With the Associated Risk Factors: A Machine Learning

Ze Yu1, Huanhuan Ji2, Jianwen Xiao3

  • 1Beijing Medicinovo Technology Co. Ltd., Beijing, China.

Frontiers in Pharmacology
|May 14, 2021
PubMed

Insights

Machine learning models can predict adverse drug events (ADEs) in pediatric patients. The Gradient Boosting Decision Tree (GBDT) model identified key risk factors, improving prediction accuracy over traditional methods.

Area of Science:

  • Pharmacovigilance
  • Computational Medicine
  • Pediatric Drug Safety

Background:

  • Adverse drug events (ADEs) pose a significant risk in pediatric populations.
  • Accurate prediction of ADEs is crucial for patient safety in hospitals.

Purpose of the Study:

  • To apply machine learning (ML) for exploring risk factors of ADEs.
  • To develop a predictive model for ADEs in Chinese pediatric inpatients.

Main Methods:

  • Utilized data from 1,746 pediatric inpatients (2013-2015).
  • Applied seven ML algorithms (XGBoost, CatBoost, AdaBoost, LightGBM, RF, GBDT, TPOT) for risk factor identification.
  • Selected Gradient Boosting Decision Tree (GBDT) for the final prediction model.

Main Results:

  • Identified key risk factors for ADEs including number of doses, BMI, number of drugs, age, and diagnoses.
  • The GBDT model achieved a precision of 44%, outperforming Global Trigger Tools (GTTs).
  • Shapley Additive exPlanations (SHAP) visualized the influence of risk factors on ADEs.

Conclusions:

  • ML, particularly GBDT, offers a novel and accurate approach for predicting ADEs in pediatric inpatients.
  • Identified risk factors provide insights for clinical risk management.
  • The developed model shows potential for future clinical application in preventing pediatric ADEs.

Keywords:
Chinese childrenadverse drug event (s)machine learningpediatricprediction

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