Predicting adverse drug events in older inpatients: a machine learning study.

Abstract

Building an effective prediction model of adverse drug events (ADE) is necessary to prevent harm caused by medication in older inpatients. This study aimed to develop a machine learning-based prediction model for the prediction of ADE and explore the risk factors associated with ADEs in older inpatients. Data were from an observational, retrospective study that included 1800 older Chinese inpatients. After dividing the patients into training and test sets (8:2), seven machine learning models were used. Demographic, admission, and treatment clinical variables were considered for model development. The discriminative performance of the model by the area under the receiver operating characteristic curve (ROC) was evaluated. We also calculated the model's accuracy, precision, recall, and F1 scores. Among 1800 patients, 296 ADEs were detected in 234 (13.00%) patients. The main cause of ADEs was antineoplastic agents (55.74%). Seven algorithms, including eXtreme Gradient Boosting (XGBoost), AdaBoost, CatBoost, Gradient Boosting Decision Tree (GBDT), Light Gradient Boosting Machine (LightGBM), Tree-based Pipeline Optimization Tool (TPOT) and Random Forest (RF), were used to establish the prediction model. The Adaboost model was chosen with the best predictive ability (accuracy 88.06%, precision 68.57%, recall 48.21%, F1 52.75%, and AUC 0.91). Ten significant factors associated with ADEs were identified, including the number of true triggers (+), length of stay, doses per patient, age, number of admissions in the previous year, surgery, drugs per patient, number of medical diagnoses, antibacterial use, and gender. Using machine learning, this novel study establishes an ADE prediction model in older patients. The sophisticated computer algorithm can be implemented at the bedside to improve patient safety in clinical practice.