A drug name could be confused because it looks or sounds like another. Nevertheless, it is not possible to know a priori the causes of the confusion. Nowadays, sophisticated similarity measures have been proposed focused on improving the score of the detection. However, when a new drug name is proposed, the Federal Drug Administration (FDA) only can reject or accept the drug name based on this value. This paper not only improves the detection of confused drug names by integrating the strengths of different similarity measures but also the orthographic and phonetic knowledge of these measures are used to give an a priori explanation of the causes of confusion. In this paper, a novel measure that integrates 24 individual measures is developed for this problem. With our proposal, each individual measure contributes to this problem. Finally, we present examples of how our proposal is used for explaining the causes of the confusion which could assist to the FDA to accept or reject a new drug name or to know the confusion causes of previously reported cases.
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