OpenExtract: Automated Data Extraction for Systematic Reviews in Health

Studies in health technology and informatics 2026 Book chapter Open access

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Authors: Jim Achterberg, Bram van Dijk, Jing Meng, Saif ul Islam, Gregory Epiphaniou, C.G. Maple, Xuefei Ding, Theodoros N. Arvanitis, Simon Brouwer, Marcel Haas, Marco Spruit
DOI: 10.3233/shti260316
Cited by: 1

Abstract

This study presents OpenExtract, an open-source pipeline for automated data extraction in large-scale systematic literature reviews. The pipeline queries large language models (LLMs) to predict data entries based on relevant sections of scientific articles. To test the efficacy of OpenExtract, we apply it to a systematic literature review in digital health and compare its outputs with those of human researchers. OpenExtract achieves precision and recall scores of > 0.8 in this task, indicating that it can be effective at extracting data automatically and efficiently. OpenExtract: https://github.com/JimAchterbergLUMC/OpenExtract.

Fields of Study & Subjects

Biomedical Text Mining and Ontologies Meta-analysis and systematic reviews Computational and Text Analysis Methods

Publication details

Journal / source
Studies in health technology and informatics
Publisher
IOS Press
Volume
336
Pages
934–938
Publication date
21 May 2026
Language
EN
Document type
Book chapter