Abstract
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 tests. During this study, we developed a real-time conversion system and method for reflowing contents in these files for accessibility practice and research data mining. Embedded information was extracted using an AI technology for text extraction and text mining to convert to structured formats. These data were then made available to veterinarians and producers via an online interface, allowing interactive searching and also presenting the commercial test assay parameters in reference to FDA-approved tolerances.
Highlights
Drug residue testing is an essential tool to ensure that animal products intended for human consumption are free of violative residues [1]
We show a summary of our workflow integrated by machine learning where first some preprocessing points are presented, and these steps are provided in detail for data extraction (Figure 1)
Two formats of documents extracted from Web sources were virtually imported for this study: files (34 tables out of 60 relevant pdf files, there were over 180 more portable document format (PDF) either did not have tables or extracted tables did not have relevant fields) were produced and made available online by seven manufacturers in PDF formats while other tables were presented by hypertext markup language (HTML) documents on Web sources
Summary
Drug residue testing is an essential tool to ensure that animal products intended for human consumption are free of violative residues [1]. Rapid quantitative drug detection has largely been applied to help minimize drug residue risks and maintain milk quality [3,4,5] To use these tests, information for different commodities including cow, swine, goat, sheep, camel, horse, and buffalo and matrices including serum, urine, milk, and honey is necessary and have been documented for approximately 100 different rapid test assays [1, 3, 6]. Contents of these documents are mostly published in semi or unstructured portable document format (PDF) files or hypertext markup language (HTML) documents which do not allow for interactive searching and easy comparison to tolerance limits
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