A proposal for a drug information database and text templates for generating package inserts.
To prevent prescription errors caused by information systems, a database to store complete and accurate drug information in a user-friendly format is needed. In previous studies, the primary method for obtaining data stored in a database is to extract drug information from package inserts by employing pattern matching or more sophisticated methods such as text mining. However, it is difficult to obtain a complete database because there is no strict rule concerning expressions used to describe drug information in package inserts. The authors' strategy was to first build a database and then automatically generate package inserts by embedding data in the database using templates. To create this database, the support of pharmaceutical companies to input accurate data is required. It is expected that this system will work, because these companies can earn merit for newly developed drugs to decrease the effort to create package inserts from scratch. This study designed the table schemata for the database and text templates to generate the package inserts. To handle the variety of drug-specific information in the package inserts, this information in drug composition descriptions was replaced with labels and the replacement descriptions utilizing cluster analysis were analyzed. To improve the method by which frequently repeated ingredient information and/or supplementary information are stored, the method was modified by introducing repeat tags in the templates to indicate repetition and improving the insertion of data into the database. The validity of this method was confirmed by inputting the drug information described in existing package inserts and checking that the method could regenerate the descriptions in the original package insert. In future research, the table schemata and text templates will be extended to regenerate other information in the package inserts.
Graduate School of Engineering and Science, Tokyo, Japan.
Ryo
R
Okuya
Masaomi
M
Kimura
Michiko
M
Ohkura
Fumito
F
Tsuchiya
cluster analysis; drug database; drug information; medical safety; package insert