Data extraction for evidence synthesis using a large language model: A proof-of-concept study.

Autor: Gartlehner G; Social, Statistical, and Environmental Sciences, RTI International, Research Triangle Park, North Carolina, USA.; Department for Evidence-based Medicine and Evaluation, Danube University Krems, Krems, Austria., Kahwati L; Social, Statistical, and Environmental Sciences, RTI International, Research Triangle Park, North Carolina, USA., Hilscher R; Social, Statistical, and Environmental Sciences, RTI International, Research Triangle Park, North Carolina, USA., Thomas I; Social, Statistical, and Environmental Sciences, RTI International, Research Triangle Park, North Carolina, USA., Kugley S; Social, Statistical, and Environmental Sciences, RTI International, Research Triangle Park, North Carolina, USA., Crotty K; Social, Statistical, and Environmental Sciences, RTI International, Research Triangle Park, North Carolina, USA., Viswanathan M; Social, Statistical, and Environmental Sciences, RTI International, Research Triangle Park, North Carolina, USA., Nussbaumer-Streit B; Department for Evidence-based Medicine and Evaluation, Danube University Krems, Krems, Austria., Booth G; Social, Statistical, and Environmental Sciences, RTI International, Research Triangle Park, North Carolina, USA., Erskine N; Preventive Medicine Residency Program, Department of Family Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA., Konet A; Social, Statistical, and Environmental Sciences, RTI International, Research Triangle Park, North Carolina, USA., Chew R; Social, Statistical, and Environmental Sciences, RTI International, Research Triangle Park, North Carolina, USA.
Jazyk: angličtina
Zdroj: Research synthesis methods [Res Synth Methods] 2024 Jul; Vol. 15 (4), pp. 576-589. Date of Electronic Publication: 2024 Mar 03.
DOI: 10.1002/jrsm.1710
Abstrakt: Data extraction is a crucial, yet labor-intensive and error-prone part of evidence synthesis. To date, efforts to harness machine learning for enhancing efficiency of the data extraction process have fallen short of achieving sufficient accuracy and usability. With the release of large language models (LLMs), new possibilities have emerged to increase efficiency and accuracy of data extraction for evidence synthesis. The objective of this proof-of-concept study was to assess the performance of an LLM (Claude 2) in extracting data elements from published studies, compared with human data extraction as employed in systematic reviews. Our analysis utilized a convenience sample of 10 English-language, open-access publications of randomized controlled trials included in a single systematic review. We selected 16 distinct types of data, posing varying degrees of difficulty (160 data elements across 10 studies). We used the browser version of Claude 2 to upload the portable document format of each publication and then prompted the model for each data element. Across 160 data elements, Claude 2 demonstrated an overall accuracy of 96.3% with a high test-retest reliability (replication 1: 96.9%; replication 2: 95.0% accuracy). Overall, Claude 2 made 6 errors on 160 data items. The most common errors (n = 4) were missed data items. Importantly, Claude 2's ease of use was high; it required no technical expertise or labeled training data for effective operation (i.e., zero-shot learning). Based on findings of our proof-of-concept study, leveraging LLMs has the potential to substantially enhance the efficiency and accuracy of data extraction for evidence syntheses.
(© 2024 The Authors. Research Synthesis Methods published by John Wiley & Sons Ltd.)
Databáze: MEDLINE
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