Improvement of Adequate Digoxin Dosage: An Application of Machine Learning Approach

Abstract EN

Digoxin is a high-alert medication because of its narrow therapeutic range and high drug-to-drug interactions (DDIs).

Approximately 50% of digoxin toxicity cases are preventable, which motivated us to improve the treatment outcomes of digoxin.

The objective of this study is to apply machine learning techniques to predict the appropriateness of initial digoxin dosage.

A total of 307 inpatients who had their conditions treated with digoxin between 2004 and 2013 at a medical center in Taiwan were collected in the study.

Ten independent variables, including demographic information, laboratory data, and whether the patients had CHF were also noted.

A patient with serum digoxin concentration being controlled at 0.5–0.9 ng/mL after his/her initial digoxin dosage was defined as having an appropriate use of digoxin; otherwise, a patient was defined as having an inappropriate use of digoxin.

Weka 3.7.3, an open source machine learning software, was adopted to develop prediction models.

Six machine learning techniques were considered, including decision tree (C4.5), k-nearest neighbors (kNN), classification and regression tree (CART), randomForest (RF), multilayer perceptron (MLP), and logistic regression (LGR).

In the non-DDI group, the area under ROC curve (AUC) of RF (0.912) was excellent, followed by that of MLP (0.813), CART (0.791), and C4.5 (0.784); the remaining classifiers performed poorly.

For the DDI group, the AUC of RF (0.892) was the best, followed by CART (0.795), MLP (0.777), and C4.5 (0.774); the other classifiers’ performances were less than ideal.

The decision tree-based approaches and MLP exhibited markedly superior accuracy performance, regardless of DDI status.

Although digoxin is a high-alert medication, its initial dose can be accurately determined by using data mining techniques such as decision tree-based and MLP approaches.

Developing a dosage decision support system may serve as a supplementary tool for clinicians and also increase drug safety in clinical practice.