[A Novel Approach to Analyze the Factors Affecting Adverse Drug Reactions by Combination of Electronic Medical Record...

Decision tree analysis, a flowchart-like tree framework, is a typical machine learning method that is widely used in various fields. The most significant feature of this method is that independent variables (e.g., with or without concomitant use of vasopressor drugs) are extracted in order of the strength of their relationship with the dependent variable to be predicted (e.g., with or without adverse drug reactions), forming a tree-like model. Specifically, users can easily and quantitatively estimate the proportion of event occurrences considering "interrelationships among multiple combinations of factors" by answering the questions in the constructed flowchart. Previously, we applied the decision tree model to vancomycin-associated nephrotoxicity and demonstrated that this method can be used to analyze the factors affecting adverse drug reactions. However, the number of cases that can be analyzed decreases significantly as the number of branches increases. Thus, many cases are necessary to generate highly accurate findings. In attempt to solve this problem, we combined big data and decision tree analyses. In this review, we present the results of our research combining big data (electronic medical record database) and a machine learning method. Furthermore, we discuss the limitations of these methods and factors to consider when applying the results of big data and machine learning analyses to clinical practice.