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.

Artificial intelligence in the field of pharmacy practice: A literature review

“…This study found that ML techniques can improve prediction accuracy for high alert drug (HAD) medication treatment, lowering the risk of ADEs, and improving medication safety. 61 Wongyikul et al created a HAD screening protocol with a ML model that used Gradient Boosting Classifier and screening parameters to identify HAD prescription errors from outpatient and inpatient drug prescriptions. The ML algorithm identified over 98% of actual HAD mismatches in the test set and 99% in the evaluation set when screening drug prescription events with a risk of HAD inappropriate use.…”

“…The risk warning platform was developed to predict PIP, PIM, and PPO, with acceptable accuracy, prediction performance, and clinical application potential. Tai, C.-T, 2020 61 Taiwan To predict the risk of high-alert medication treatment (digoxin) using machine-learning techniques Retrospective analysis This study included patients who had accepted digoxin therapy while hospitalized between January 2004 and December 2013. AUC values ranged from 0.551 to 0.836.…”

Machine Learning: A New Approach for Dose Individualization

“…After removing duplicated articles, 3,346 studies were screened by the title and/or abstract, 3,175 irrelevant studies were excluded and 171 articles were included for full‐text review. Finally, 64 articles related to precision dosing using ML were included for analysis 11–74 . The PRISMA flow diagram representing the study selection process and review results is presented in Figure .…”

Detecting Digoxin Toxicity by Artificial Intelligence-Assisted Electrocardiography

“…Previous machine learning studies used several variables including drug-to-drug interaction, demographic items, laboratory items, and medical history to set up optimal SDC prediction models with the AUC ranging from 0.533 to 0.912 [ 29 , 30 ]. Importantly, our deep learning model predicted high-risk digoxin toxicity patients by ECG only with the AUC of 0.912.…”