Developing AI Methods for Animal Health and Welfare Monitoring

Large amounts of free text data are collected as part of animal welfare research, usually in the form of veterinary clinical notes and free text responses from citizen science surveys. However, free text data often isn't utilized as fully as quantitative and qualitive data collected from animal welfare studies as it is typically poorly structured, grammatically variable and over-abbreviated and therefore hard to extract signals from. The aim of this PhD project is to extract health and welfare signals from this type of messy free text.
The data will be provided by two of the largest collectors of this data type in the UK, SAVSNET (Small Animal Veterinary Surveillance Network) and Dogs Trust. The data collected by SAVSNET is real time data from over 400 UK veterinary practices including breed, sex, neuter status, prescribed drugs, syndromic labels describing main presenting complaint and narrative free text records from veterinary clinical notes. The data collected by Dogs Trust is free text data from citizen science surveys generated and hosted by Dogs Trust. These surveys are both longitudinal (e.g., Generation Pup, n ~ 8,000; Post Adoption Welfare Study, n ~9,000), and one-off research projects (National Dog Survey, n = 250,000; Choosing My Dog, n ~ 10,000) with free text collected typically documenting owner accounts of canine behavioural history or concerns and opinions of owners regarding their dog. Dogs Trust also possess some veterinary clinical notes in the form of narrative free text in PDF format.
The intention is that signals will be retrieved from this data which will be of benefit to animal welfare research. To extract these signals, both supervised and unsupervised machine learning methods will be used. These methods will make use of transformers such as BERT and open source large language models such as Llama and Falcon, optimized for use on language used by vets and survey participants to establish for trends from the text. By doing this, we aim to achieve several outcomes. These include (but are not limited to):
1. Being able to extract data in a standardised format from PDF veterinary clinical notes (e.g. main presenting complaints and treatment plans)
2. Creating pipelines for evaluating emerging health and welfare trends in dogs (e.g. new virus outbreaks)
3. Developing an early warning system using Dogs Trust owner reported data to highlight records that require support or follow ups (e.g. instances of high-risk problem behaviours, occurrences of domestic and/or animal abuse)
By achieving these aims, we hope to increase both access to and insight from data held by SAVSNET and Dogs Trust, allowing for the expansion of canine health and welfare studies.