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.
Highlights
Since To Err is Human published by the Institute of Medicine (IOM) in 1999, patient safety has become a global concern [1]
area under the curve (AUC), F-measure, precision, and recall were used to assess the performance of the prediction models
The results showed that the performance of random forest (RF) classifier was the best (0.836; excellent discrimination), followed by C4.5 (0.719) and artificial neural network (ANN) (0.688); the remaining classifiers performed poorly
Summary
Since To Err is Human published by the Institute of Medicine (IOM) in 1999, patient safety has become a global concern [1]. Nonprofit organizations and medical institutions have proposed various measures and invested much resources to improve patient safety in the past. 20 years, incidents of patients being injured due to improper medical care continue to occur every day around the world [2]. The avoidance of medical injuries to maintain patient safety is still a difficult problem to solve [3]. The World Health Organization (WHO) and the World Alliance for Patient Safety have jointly proposed two Global Patient Safety Challenges, which provide improvement measures and methods for patient safety issues to reduce the possible harm to patients during medical care.
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