## 4 Discussion  
In recent years, the patient safety of high-alert medication treatment is an urgent issue, especially cardiovascular drugs [1; 3]. Previous studies have employed various methods, such as statistical models and pharmacokinetics, to enhance the safety of using high-alert medications. In addition, machine-learning techniques (e.g., ANN) have lately been used to enhance the predictive power of models. However, because the decision-making process of ANN cannot be known and lack of explanatory power, its use in clinical applications is still limited. Our study investigated various well-known machine-learning techniques. The results indicated that, compared with other techniques (kNN, LGR and SVM), the average performance of decision tree-based methods was superior to that of other techniques. In addition, decision tree-based methods yielded the information of decision rules that can support physicians in making treatment decision of high-alert medications. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **AUC** & **Precision** & **Recall** & **F-measure** \\ \hline Male & 0.834 & 0.875 & 0.824 & 0.848 \\ Female & 0.848 & 0.901 & 0.753 & 0.820 \\ With CHF * & 0.804 & 0.845 & 0.803 & 0.824 \\ Without CHF * & 0.809 & 0.866 & 0.640 & 0.736 \\ With DDI * & 0.848 & 0.840 & 0.794 & 0.816 \\ Without DDI * & 0.750 & 0.793 & 0.676 & 0.730 \\ \hline \hline \end{tabular}  
* CHF—congestive heart failure; DDI—drug-to-drug interaction.  
\end{table} 
Table 7: Results of classification performance in specific scenarios. Figure 1: The receiver operating characteristic (ROC) curve of RF classifier. In addition to comparing the prediction capabilities of different classification techniques, we further analyzed the influence of each variable on the occurrence of ADEs to provide a reference for clinicians. By computing gain ratios, we disclosed that the most important variables affecting the outcome of the treatment are shown in Table 8. After discussion with the medical specialists, including 2 cardiologists, 1 clinical nurse specialists and 3 clinical pharmacists, the validity of the results is discussed as follows:  
First, BUN and Scr are commonly used indicators of kidney function. A raised BUN/SCr ratio will increase the hospitalization risk and mortality of CHF patients [40]. [PERSON] et al. found that the higher the BUN value, the more digoxin is accumulated in the body. Because digoxin is primarily excreted by kidneys, low kidney function may cause the rise of SDC and lead to ADEs such as digoxin toxicity [41]. Our study confirmed that kidney function is highly correlated with the risk of ADEs. This finding is similar with the outcomes of previous studies about digoxin treatment [42; 43]. Therefore, physicians need to adjust the dose when decided to prescribe digoxin to patients with renal insufficiency. Furthermore, we also found that DDI is a significant factor of treatment outcome in high-alert medications. DDI has long received considerable attention in the field of medicine, especially involving high-alert medications, which has a complex pharmacological mechanism [16; 17; 44; 45; 46; 47; 48; 49]. The digoxin related DDIs can easily cause toxicity reaction and ADEs [50]. Although our study only considered drugs that will produce major degree DDI with digoxin, these results are still adequate to confirm the significance of DDI. Finally, the adequacy of the initial dose is important in high-alert medication therapy. Inappropriate doses resulting in high plasma drug concentrations can lead to severe ADEs that influence the treatment outcome, particularly digoxin [51]. Therefore, previous studies have focused on novel approaches to predict the adequate dose of digoxin [44; 45; 46; 47; 48; 49; 51]. In practice, the initial dose is considerably different from the maintenance dose, which may increase the risk of ADEs before reaching a stable maintenance dose.