Abstract
The ability to efficiently derive insights from wildlife necropsy data is essential for advancing conservation and One Health objectives, yet close reading remains the mainstay of knowledge retrieval from ubiquitous free-text clinical data. This time-consuming process poses a barrier to the efficient utilisation of such valuable resources. This study evaluates part of a bespoke text-mining application, DEE (Describe, Explore, Examine), designed for extracting insights from free-text necropsy reports housed in Aotearoa New Zealand's Wildbase Pathology Register. A pilot test involving nine veterinary professionals assessed DEE's ability to quantify the occurrence of four clinicopathologic findings (external oiling, trauma, diphtheritic stomatitis, and starvation) across two species datasets by comparison to manual review. Performance metrics-recall, precision, and F1-score-were calculated and analysed alongside tester-driven misclassification patterns. Findings reveal that while DEE (and the principals underlying its function) offers time-efficient data retrieval, its performance is influenced by search term selection and the breadth of vocabulary which may describe a clinicopathologic finding. Those findings characterized by limited terminological variance, such as external oiling, yielded the highest performance scores and the most consistency across application testers. Mean F1-scores across all tested findings and application testers was 0.63-0.93. Results highlight the utility and limitations of term-based text-mining approaches and suggests that enhancements to automatically capture this terminological variance may be necessary for broader implementation. This pilot study highlights the potential of relatively simple, rule-based text-mining approaches to derive insights natural language wildlife data in the support of One Health goals.