ArticleFrontiers in pharmacology2021
Predicting Adverse Drug Events in Chinese Pediatric Inpatients With the Associated Risk Factors: A Machine Learning Study.
Article in Frontiers in pharmacology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 36 citations in OpenAlex.
- Machine learning to predict adverse drug events based on electronic health records: a systematic review and meta-analysis.The Journal of international medical research · 2024Pooled it
- Polypharmacy and adverse drug reactions among hospitalised children: determinants and clinical and economic impact in a multicentre prospective cohort and nested matched case-control study in Northwest Ethiopia.BMJ paediatrics open · 2026Article
- Developmental trajectories of vocabulary and logical thinking skills across adolescence: a person-centered longitudinal study in China.BMC public health · 2026Article
- The tree-based pipeline optimization tool: Tackling biomedical research problems with genetic programming and automated machine learning.Patterns (New York, N.Y.) · 2025Article
- Risk Factors Associated With Drug-Related Side Effects in Children: A Scoping Review.Global pediatric health · 2024Article
- A proposed tree-based explainable artificial intelligence approach for the prediction of angina pectoris.Scientific reports · 2023Article
- Data harnessing to nurture the human mind for a tailored approach to the child.Pediatric research · 2023Review
- Machine Learning Model in Predicting Sarcopenia in Crohn's Disease Based on Simple Clinical and Anthropometric Measures.International journal of environmental research and public health · 2022Article
- Ultrasound-based radiomics XGBoost model to assess the risk of central cervical lymph node metastasis in patients with papillary thyroid carcinoma: Individual application of SHAP.Frontiers in oncology · 2022Article
- A Risk-Factor Model for Antineoplastic Drug-Induced Serious Adverse Events in Cancer Inpatients: A Retrospective Study Based on the Global Trigger Tool and Machine Learning.Frontiers in pharmacology · 2022Article
- Establishment and Optimization of Radiomics Algorithms for Prediction of KRAS Gene Mutation by Integration of NSCLC Gene Mutation Mutual Exclusion Information.Frontiers in pharmacology · 2022Article
- Application of an Interpretable Machine Learning Model to Predict Lymph Node Metastasis in Patients with Laryngeal Carcinoma.Journal of oncology · 2022Article
- Predictive modeling of pediatric drug-induced liver injury: Dynamic classifier selection with clustering analysis.Digital healthArticle
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Authors and funding
12 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of this study was to apply machine learning methods to deeply explore the risk factors associated with adverse drug events (ADEs) and predict the occurrence of ADEs in Chinese pediatric inpatients. Data of 1,746 patients aged between 28 days and 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 ADE occurrence, of which the most common drugs inducing ADEs were antibacterials. 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 Global Trigger Tools (GTTs) for ADE 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.
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