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Extra-label drug use in food animal medicine is authorized by the US AnimalMedicinal Drug Use Clarification Act (AMDUCA), and estimated withdrawalintervals are based on published scientific pharmacokinetic data. Occasionallythere is a paucity of scientific data on which to base a withdrawal interval ora large number of animals being treated, driving the need to test for drugresidues. Rapid assay commercial farm-side tests are essential for monitoringdrug residues in animal products to protect human health. Active ingredients,sensitivity, matrices, and species that have been evaluated for commercialrapid assay tests are typically reported on manufacturers' websites or in PDFdocuments that are available to consumers but may require a special accessrequest. Additionally, this information is not always correlated withFDA-approved tolerances. Furthermore, parameter changes for these tests can bevery challenging to regularly identify, especially those listed on websites orin documents that are not publicly available. Therefore, artificialintelligence plays a critical role in efficiently extracting the data andensure current information. Extracting tables from PDF and HTML documents hasbeen investigated both by academia and commercial tool builders. Research intext mining of such documents has become a widespread yet challenging arena inimplementing natural language programming. However, techniques of extractingtables are still in their infancy and being investigated and improved byresearchers. In this study, we developed and evaluated a data-mining method forautomatically extracting rapid assay data from electronic documents. Ourautomatic electronic data extraction method includes a software package module,a developed pattern recognition tool, and a data mining engine. Assay detailswere provided by several commercial entities that produce these rapid drugresidue assay

Large-Scale Data Mining of Rapid Residue Detection Assay Data From HTML and PDF Documents: Improving Data Access and Visualization for Veterinarians