Automated Extraction of Pharmacokinetic Parameters from Structured XML Scientific Articles: Enhancing Data Accessibility at Scale

TL;DR

This paper addresses the challenge of automating the extraction of pharmacokinetic data from complex tabular XML documents in scientific publications to improve data accessibility and support pharmacology research.

Contribution

The authors develop an AI-based method for accurately detecting and extracting pharmacokinetic parameters from structured XML tables in scientific articles.

Findings

Automated extraction achieves high accuracy in identifying PK parameters.

The method handles diverse table layouts and complex structures.

Improves data collection efficiency for pharmacology research.

Abstract

In the field of pharmacology, there is a notable absence of centralized, comprehensive, and up-to-date repositories of PK data. This poses a significant challenge for R&D as it can be a time-consuming and challenging task to collect all the required quantitative PK parameters from diverse scientific publications. This quantitative PK information is predominantly organized in tabular format, mostly available as XML, HTML, or PDF files within various online repositories and scientific publications, including supplementary materials. This makes tables one of the crucial components and information elements of scientific or regulatory documents as they are commonly utilized to present quantitative information. Extracting data from tables is typically a labor-intensive process, and alternative automated machine learning models may struggle to accurately detect and extract the relevant data due to the complex nature and diverse layouts of tabular data. The difficulty of information extraction and reading order detection is largely dependent on the structural complexity of the tables. Efforts to understand tables should prioritize capturing the content of table cells in a manner that aligns with how a human reader naturally comprehends the information. FARAD has been manually extracting tabular data and other information from literature and regulatory agencies for over 40 years. However, there is now an urgent need to automate this process due to the large volume of publications released daily. The accuracy of this task has become increasingly challenging, as manual extraction is tedious and prone to errors, especially given the staffing shortages we are currently facing. This necessitates the development of AI algorithms for table detection and extraction that are able to precisely handle cells organized according to the table structure, as indicated by column and/or row header information.