Accelerating Disease Model Parameter Extraction: An LLM-Based Ranking Approach to Select Initial Studies for Literature Review Automation

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dc.citation.issue2
dc.citation.volume7
dc.contributor.authorSujau M
dc.contributor.authorWada M
dc.contributor.authorVallée E
dc.contributor.authorHillis N
dc.contributor.authorSušnjak T
dc.contributor.editorVerspoor K
dc.date.accessioned2025-04-02T19:16:25Z
dc.date.available2025-04-02T19:16:25Z
dc.date.issued2025-03-26
dc.description.abstractAs climate change transforms our environment and human intrusion into natural ecosystems escalates, there is a growing demand for disease spread models to forecast and plan for the next zoonotic disease outbreak. Accurate parametrization of these models requires data from diverse sources, including the scientific literature. Despite the abundance of scientific publications, the manual extraction of these data via systematic literature reviews remains a significant bottleneck, requiring extensive time and resources, and is susceptible to human error. This study examines the application of a large language model (LLM) as an assessor for screening prioritisation in climate-sensitive zoonotic disease research. By framing the selection criteria of articles as a question–answer task and utilising zero-shot chain-of-thought prompting, the proposed method achieves a saving of at least 70% work effort compared to manual screening at a recall level of 95% (NWSS 95%). This was validated across four datasets containing four distinct zoonotic diseases and a critical climate variable (rainfall). The approach additionally produces explainable AI rationales for each ranked article. The effectiveness of the approach across multiple diseases demonstrates the potential for broad application in systematic literature reviews. The substantial reduction in screening effort, along with the provision of explainable AI rationales, marks an important step toward automated parameter extraction from the scientific literature.
dc.description.confidentialfalse
dc.description.notesarticle-number: 28
dc.edition.editionJune 2025
dc.identifier.citationSujau M, Wada M, Vallée E, Hillis N, Sušnjak T. (2025). Accelerating Disease Model Parameter Extraction: An LLM-Based Ranking Approach to Select Initial Studies for Literature Review Automation. Machine Learning and Knowledge Extraction. 7. 2.
dc.identifier.doi10.3390/make7020028
dc.identifier.eissn2504-4990
dc.identifier.elements-typejournal-article
dc.identifier.number28
dc.identifier.urihttps://mro.massey.ac.nz/handle/10179/72723
dc.languageEnglish
dc.publisherMDPI (Basel, Switzerland)
dc.publisher.urihttp://10.0.13.62/make7020028
dc.relation.isPartOfMachine Learning and Knowledge Extraction
dc.rights(c) The author/sen
dc.rights.licenseCC BYen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectlarge language models in systematic reviews
dc.subjectautomated AI literature screening
dc.subjectzero-shot relevancy ranking
dc.subjectclimate-sensitive zoonotic disease modelling
dc.subjectinformation retrieval in medical literature
dc.subjectsystematic literature review automation
dc.subjectbiomedical text mining for disease tracking
dc.subjectAI-assisted disease surveillance
dc.titleAccelerating Disease Model Parameter Extraction: An LLM-Based Ranking Approach to Select Initial Studies for Literature Review Automation
dc.typeJournal article
pubs.elements-id500236
pubs.organisational-groupOther
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