Abstract
Manual review of necropsy records through close reading and collation is a time-consuming process, leading to delays in knowledge acquisition, communication of findings, and subsequent actions. Text-mining techniques offer a means to reduce these barriers by automating the extraction of information from large volumes of free-text clinical reports, minimizing the need for manual review. Additionally, interactive dashboards enable end users to interrogate data dynamically, tailoring analyses to their specific needs and objectives. Here, we describe the principles underlying an application designed to extract and visualize information from free-text necropsy records within the Wildbase Pathology register. Reflecting the structure of a traditional necropsy review-where each record is examined in detail to identify and collate key observations-the application is divided into three sections. The first allows a user to upload a dataset in comma separated value format as downloaded from the Wildbase Pathology Register. A user can then filter and interrogate selected signalment variables of the population within this dataset. The second section uses established text-mining calculations of word correlations and Latent Dirichlet Allocation to generate visualisations to give a user a subjective sense of common themes found within the uploaded data. The third and final section uses a custom rule-based algorithm to identify and quantify positive occurrences of clinicopathologic findings as input by an end user. The foundational methods employed in this application have the potential for broader application in veterinary and medical pathology, facilitating more efficient and timely access to critical insights.