###### 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. machine learning; high-alert medication; cardiovascular drug; adverse drug events; decision tree +
Footnote †: journal: Medical  
+++ ==WARNING: Truncated because of repetitions==
released the third global patient safety challenge which indicates that if medication errors occur in the medical system, it may cause serious harm to patients [4]. In the United States, medication errors cause at least one death each day and injure nearly 1.3 million people each year [4]. It is estimated that the additional medical costs worldwide caused by medication errors amount to US $42 billion per year, accounting for 0.7% of global healthcare expenditure [5]. Compared with ordinary drugs, high-alert medications carry greater risks of adverse drug events (ADEs) and have received widespread attention [6]. The definition of high-alert medications is drugs that bear a heightened risk of causing significant patient harm when they are used in error [7]. Therefore, although high-alert medications account for only a small portion of all therapeutic drugs, they are the main cause of adverse drug events (ADEs). Recent studies have shown that up to 50% of ADEs are related to high-alert medications [8]. Medication errors will not only lead to an increase in medical costs, but also cause varying degrees of harm to patients, especially high-alert medications. To date, clinical decision support systems (CDSS) have been widely and maturely applied to most drugs. However, the current application of CDSS in high-alert medications is mainly based on the recommendations of the Clinical Practice Guideline (CPG) to establish prompts in the system, rather than generating predictions of treatment results. Therefore, some scholars have begun to use machine-learning techniques to conduct research on specific high-alert medications [9; 10]. The purpose is to provide better clinical decision support to enhance the safety of high-alert medication treatment. Cardiovascular disease (CVD) is currently the main cause of death in the world [11]. According to WHO statistics, approximately 17.65 million people worldwide died of CVD in 2016, accounting for 31% of all deaths, indicating that approximately one in three people died of CVD. The number of people who have died of CVD is not only twice as high as that of cancer, but more than the sum of all deaths from infectious diseases [12]. Similarly, CVD is also the most common cause of death in Europe.