Extended results for: enhancing abstract screening classification in evidence-based medicine: incorporating domain knowledge into pre-trained models.

Ofori-Boateng, Regina; Aceves-Martins, Magaly; Wiratunga, Nirmalie; Moreno-Garcia, Carlos

Abstract

Evidence-based medicine (EBM) is a foundational element in medical research, playing a crucial role in shaping healthcare policies and clinical decision-making. However, the rigorous processes required for EBM, particularly during the abstract screening phase, pose substantial challenges to researchers. While many have sought to automate this stage using Pre-trained Language Models (PLMs), these efforts often face obstacles due to the specificity of the domain, especially when dealing with EBM studies related to both human and animal subjects. To address this, our initial research presented a state-of-the-art (SOTA) transfer learning approach that enhanced four abstract screening by embedding domain-specific knowledge into PLMs without modifying their base weights utilising the concepts of adapters. Extending the previous work, in this study, we evaluate the same methodology on four animal and human EBM datasets. Our evaluation, conducted on the further four EBM abstract screening datasets, demonstrates that the proposed method significantly improves the screening process and outperforms strong baseline PLMs.

Citation

OFORI-BOATENG, R., ACEVES-MARTINS, M., WIRATUNGA, N. and MORENO-GARCIA, C.F. 2024. Extended results for: enhancing abstract screening classification in evidence-based medicine: incorporating domain knowledge into pre-trained models. In Martin, K., Salimi, P. and Wijayasekara, V. (eds.). Proceedings of the 2024 SICSA (Scottish Informatics and Computer Science Alliance) REALLM (Reasoning, explanation and applications of large language models) workshop (SICSA REALLM workshop 2024), 17 October 2024, Aberdeen, UK. CEUR workshop proceedings, 3822Aachen: CEUR-WS [online], pages 11-18. Available from: https://ceur-ws.org/Vol-3822/short1.pdf

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2024 SICSA (Scottish Informatics and Computer Science Alliance) REALLM (Reasoning, explanation and applications of large language models) workshop (SICSA REALLM workshop 2024)
• Start Date: Oct 17, 2024
• Acceptance Date: Oct 3, 2024
• Online Publication Date: Oct 17, 2024
• Publication Date: Oct 17, 2024
• Deposit Date: Nov 20, 2024
• Publicly Available Date: Dec 3, 2024
• Publisher: CEUR-WS
• Peer Reviewed: Peer Reviewed
• Pages: 11-18
• Series Title: CEUR workshop proceedings
• Series Number: 3822
• Series ISSN: 1613-0073
• Keywords: Evidence-based medicine; Domain integration; Large/pre-trained language models; Transfer learning
• Public URL: https://rgu-repository.worktribe.com/output/2584551
• Publisher URL: https://ceur-ws.org/Vol-3822/
• Related Public URLs: https://rgu-research.worktribe.com/record.jx?recordid=2418785 (original article to which this output relates to)