Quantification of misclassification categorisation. Misclassification categories quantified across all testers and clinicopathologic findings for false positives and false negatives in a pilot test of the text-mining application; DEE, designed for the rapid retrieval of necropsy data from free-text reports within the Wildbase Pathology Register of Aotearoa New Zealand.

<p>Quantification of misclassification categorisation. Misclassification categories quantified across all testers and clinicopathologic findings for false positives and false negatives in a pilot test of the text-mining application; DEE, designed for the rapid retrieval of necropsy data from f...

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Bibliographic Details
Main Author:	Stefan Saverimuttu (20882325) (author)
Other Authors:	Kate McInnes (7520158) (author), Kristin Warren (3339657) (author), Lian Yeap (21221659) (author), Stuart Hunter (2498485) (author), Brett Gartrell (6325817) (author), An Pas (12001965) (author), James Chatterton (20347204) (author), Bethany Jackson (3339660) (author)
Published:	2025
Subjects:	Ecology Information Systems not elsewhere classified wildlife necropsy data wildbase pathology register study evaluates part score &# 8212 pilot study highlights one health objectives limited terminological variance examine ), designed driven misclassification patterns consuming process poses analysed alongside tester 63 &# 8211 terminological variance may efficiently derive insights text clinical data search term selection efficient data retrieval four clinicopathologic findings highest performance scores scores across knowledge retrieval extracting insights efficient utilisation clinicopathologic finding tested findings findings reveal findings characterized xlink "> valuable resources results highlight relatively simple rapid analysis principals underlying mining approaches mining application may describe manual review external oiling diphtheritic stomatitis broader implementation bespoke text based text automatically capture application testers advancing conservation
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