Machine Learning in High-Alert Medication Treatment: A Study on the Cardiovascular Drug

Abstract

The safety of high-alert medication treatment is still a challenge all over the world. Approximately one-half of adverse drug events (ADEs) are related to high-alert medications, which motivates us to improve the predicament faced in clinical practice. The purpose of this study is to use machine-learning techniques to predict the risk of high-alert medication treatment. Taking the cardiovascular drug digoxin as an example, we collected the records of 513 patients who received the pertinent therapy during hospitalization at a tertiary medical center in Taiwan. Considering serum digoxin concentration (SDC) is the primary indicator for assessing the risk of digoxin therapy, patients with SDC being controlled at the recommended range before their discharge were defined as a low-risk population; otherwise, patients were defined as the high-risk population. Weka 3.9.4—an open source machine learning software—was adopted to develop binary classification models to predict the risk of digoxin therapy by a number of machine-learning techniques, including k-nearest neighbors (kNN), decision tree (C4.5), support vector machine (SVM), random forest (RF), artificial neural network (ANN) and logistic regression (LGR). The results showed that the performance of RF was the best, followed by C4.5 and ANN; the remaining classifiers performed poorly. This study confirmed that machine-learning techniques can yield favorable prediction effectiveness for high-alert medication treatment, thereby decreasing the risk of ADEs and improving medication safety.

Keywords

• machine learning
• high-alert medication
• cardiovascular drug
• adverse drug events
• decision tree