Automatic Identification of Confusable Drug Names
Many hundreds of drugs have names that either look or sound so much
alike that doctors, nurses and pharmacists can get them confused,
dispensing the wrong one in errors that can injure or even kill
We propose to address the problem through the
application of two new methods---one based on orthographic
and the other based on phonetic similarity ("sound-alike").
In order to compare the effectiveness of the new methods
for identifying confusable drug names with other known similarity
we developed a novel evaluation methodology.
We show that the new orthographic measure
(BI-SIM) outperforms other commonly used measures of similarity on a
set containing both look-alike and sound-alike pairs, and that a new
feature-based phonetic approach (ALINE) outperforms orthographic
approaches on a test set containing solely sound-alike pairs.
However, an approach that combines several different measures
achieves the best results on two test sets.
Our system is currently used as the basis of a system developed for
the U.S. Food and Drug Administration for detection of confusable drug