AUTHOR=Yu Ze , Ji Huanhuan , Xiao Jianwen , Wei Ping , Song Lin , Tang Tingting , Hao Xin , Zhang Jinyuan , Qi Qiaona , Zhou Yuchen , Gao Fei , Jia Yuntao 

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

JOURNAL=Frontiers in Pharmacology

VOLUME=Volume 12 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2021.659099

DOI=10.3389/fphar.2021.659099

ISSN=1663-9812

ABSTRACT=The aim of this study was to apply machine learning methods to deep explore the risk factors associated with adverse drug events (ADEs) and predict the occurrence of ADEs in Chinese pediatric inpatients. Data of 1746 patients aged between 28 days to 18 years (mean age = 3.84 years) were included in the study from January 1, 2013 to December 31, 2015 in the Children’s Hospital of Chongqing Medical University. There were 247 cases of ADEs occurrence, of which the most common drugs inducing ADEs were anti-bacterials. Seven algorithms including eXtreme Gradient Boosting (XGboost), Catboost, AdaBoost, LightGBM, Random Forest (RF), Gradient Boosting Decision Tree (GBDT) and TPOT were used to select the important risk factors, and GBDT was chosen to establish the prediction model with the best predicting abilities (precision=44%, recall=25%, F1=31.88%). The GBDT model has better performance than using Global Trigger Tools (GTT) for ADEs prediction (precision 44% vs 13.3%). In addition, multiple risk factors were identified via GBDT, such as the number of trigger true (TT) (+), number of doses, BMI, number of drugs, number of admission, height, length of hospital stay, weight, age and number of diagnoses. The influencing directions of the risk factors on ADEs were displayed through Shapley Additive exPlanations (SHAP). This study provides a novel method to accurately predict adverse drug events in Chinese pediatric inpatients with the associated risk factors, which may be applicable in clinical practice in the future.