↓ Skip to main content

Dove Medical Press

A proposal for a drug information database and text templates for generating package inserts

Overview of attention for article published in Drug, Healthcare and Patient Safety, July 2013
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#45 of 162)
  • Good Attention Score compared to outputs of the same age (79th percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

Mentioned by

twitter
2 X users
patent
2 patents

Readers on

mendeley
10 Mendeley
Title
A proposal for a drug information database and text templates for generating package inserts
Published in
Drug, Healthcare and Patient Safety, July 2013
DOI 10.2147/dhps.s43303
Pubmed ID
Authors

Ryo Okuya, Masaomi Kimura, Michiko Ohkura, Fumito Tsuchiya

Abstract

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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 10 100%

Demographic breakdown

Readers by professional status Count As %
Student > Postgraduate 3 30%
Student > Ph. D. Student 2 20%
Student > Doctoral Student 1 10%
Unspecified 1 10%
Lecturer > Senior Lecturer 1 10%
Other 1 10%
Unknown 1 10%
Readers by discipline Count As %
Medicine and Dentistry 4 40%
Computer Science 2 20%
Social Sciences 1 10%
Unspecified 1 10%
Unknown 2 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 19 July 2022.
All research outputs
#4,947,179
of 25,806,080 outputs
Outputs from Drug, Healthcare and Patient Safety
#45
of 162 outputs
Outputs of similar age
#39,296
of 207,643 outputs
Outputs of similar age from Drug, Healthcare and Patient Safety
#1
of 5 outputs
Altmetric has tracked 25,806,080 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 162 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.7. This one has gotten more attention than average, scoring higher than 66% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 207,643 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 79% of its contemporaries.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them