Personal Statement
I work in the Small Animal teaching Hospital at the Leahurst Campus as part of the internal medicine team. Here I oversee internal medicine referral cases and train undergraduate students in small animal internal medicine. With nearly thirty years of experience, I enjoy encouraging students to develop the complex, integrative thought process required to manage these cases.
I am also a PI on the Small Animal Surveillance Network (SAVSNET) project. SAVSNET collates a growing database with over 15 million clinical notes from over 400 veterinary premises across the UK. My key interest here is text-mining clinical notes to gain insights in to companion animal health. This brings together interests in small animal medicine and computer programming. I have written a suite of tools (SAVSNET Datalab) used by undergraduate and post-graduate students to pursue text-mining projects using SAVSNET data. Through collaborations with the Departments of Computer Science at Liverpool and Manchester and Durham University, I am also pursuing the use of the latest methodologies in artificial intelligence using generative models like ChatGPT, Gemini and Llama and their smaller cousins (bidirectional encoder representations using transformers or BERT for short) to automate record-reading and unlock the potential of the SAVSNET database.

Research Overview
My research is centered around automating information extraction from large volumes of veterinary clinical notes using text mining methods that include machine learning and AI. We are evaluating the use of transformers (neural network language models that capture subtle details of word context) to help automate capturing meaning from messy clinical notes. We are also evaluating large language models (like ChatGPT) for their utility in extracting data from clinical text. There has never been a more exciting time to work in this field.