As part of Iechyd Da’s commitment to The University of Liverpool’s FAVSNET project(our ADGC workstream on practice syndromic surveillance ), Iechyd Da have part sponsored a PhD with the institution.
Interested in doing a PhD in this rapidly evolving and groundbreaking subject-take a look!
How can artificial intelligence and data science be used to complement / augment trusted veterinary professionals to improve understanding of farm animal health and disease
About the Project
Much of farm animal surveillance currently relies on passive reporting of notifiable diseases by owners and vets alike, passive surveillance of samples submitted to laboratories, and active surveillance of diseases like bovine tuberculosis. This leaves a gap in our understanding of population-level disease, namely what is being seen in primary veterinary care and on farm.
Most veterinary surgeons now manage their clinical records digitally; these electronic health records (EHRs) represent a research and surveillance opportunity. In animal health, use of EHRs is best developed in companion animals where digitisation of individual animal health records is most complete.
As part of a wider programme of work focussing on understanding and mitigating AMR in Wales (Arwain DGC), we have been piloting the ethics and usability of collecting EHR data from a sentinel network of farm animal practices in Wales (FAVSNET).
In this PhD, you will leverage these records that are collected in real-time and added to daily. Your specific objectives will be to:
- Use supervised and unsupervised language models to develop methods to extract useful clinical syndromic and diagnostic information from unstructured clinical narratives.
- Test the ability of language model approaches to obtain treatment data at the population level including metrics of dose, frequency and number of animals treated.
- To develop clinically useful and meaningful metrics of accuracy for disease and treatment.
- To work with stakeholders including farmers and practitioners to assess metrics of AI maturity / acceptability in support of real-world practice.
- To combine outputs from 1 and 2 and carry out an interventional trial to assess our ability to improve antibacterial use on high using farms in FAVSNET at the syndrome level.
Placements with lechyd Da will be arranged at several times through the project timed to develop a wider understanding of the context of the project and to facilitate best knowledge transfer from research into practice.
You will have a demonstrable interest in animal health and data science. Whilst a veterinary or bioveterinary-related degree may be desirable it is not essential. But an ability to communicate and work with vets and farmers will be essentials. For those without existing computing-related qualifications, it will be essential to demonstrate a strong desire to learn them.
You will be based in the wider data science group at University of Liverpool Leahurst campus (including those working with small animal and equine data). The group regularly attends text mining conferences / workshops – indeed Nenadic is an organiser of the annual HealTAC Healthcare Text Analytics Conference. At the recent Cornell Vet Ai symposium, members of the group won three of the available prizes, testament to the standing of our work. Members of the group also regularly take part in public engagement events, and the candidate will be encouraged to do the same, as a way of developing a deep understanding of the challenges and joys of communicating about AI science.
For details see the link below.
