  Extra-label drug use in food animal medicine is authorized by the US Animal
Medicinal Drug Use Clarification Act (AMDUCA), and estimated withdrawal
intervals are based on published scientific pharmacokinetic data. Occasionally
there is a paucity of scientific data on which to base a withdrawal interval or
a large number of animals being treated, driving the need to test for drug
residues. Rapid assay commercial farm-side tests are essential for monitoring
drug residues in animal products to protect human health. Active ingredients,
sensitivity, matrices, and species that have been evaluated for commercial
rapid assay tests are typically reported on manufacturers' websites or in PDF
documents that are available to consumers but may require a special access
request. Additionally, this information is not always correlated with
FDA-approved tolerances. Furthermore, parameter changes for these tests can be
very challenging to regularly identify, especially those listed on websites or
in documents that are not publicly available. Therefore, artificial
intelligence plays a critical role in efficiently extracting the data and
ensure current information. Extracting tables from PDF and HTML documents has
been investigated both by academia and commercial tool builders. Research in
text mining of such documents has become a widespread yet challenging arena in
implementing natural language programming. However, techniques of extracting
tables are still in their infancy and being investigated and improved by
researchers. In this study, we developed and evaluated a data-mining method for
automatically extracting rapid assay data from electronic documents. Our
automatic electronic data extraction method includes a software package module,
a developed pattern recognition tool, and a data mining engine. Assay details
were provided by several commercial entities that produce these rapid drug
residue assay
