Mr Noel Kennedy
BA MPhil
Head of Software Development
Department: Professional Services
Campus: Camden
Research Centres: Veterinary Epidemiology, Economics and Public Health
I am the Head of software development. I am a software engineer with a particular focus on machine-learning applications. I am tech lead on the VetCompass project.
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Improved Stewardship to Protect Veterinary Antimicrobial Usage in UK Cats and Dogs
This study aims to identify and promote effective antimicrobial stewardship interventions in UK first-opinion companion animal practice. Our objectives are to benchmark current antimicrobial usage in dogs and cats, develop and implement a range of antimicrobial stewardship interventions, evaluate the impact of these interventions, and promote widespread adoption of the most effective interventions across the UK.
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Improved Stewardship to Protect Veterinary Antimicrobial Usage in UK Cats and Dogs
People: David Brodbelt, Dan O'Neill, Sian Frosini, Noel Kennedy, David Church
Vet Compass Project Type: Dog, Cat, Small Animal
Funded by the Petplan Charitable Trust, this project aims to identify and promote effective and practical antimicrobial stewardship interventions for widespread adoption in veterinary practice.
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Vet Compass Project
VetCompass is a welfare-focussed epidemiological research project that shares anonymised clinical data from veterinary practices to investigate the frequency and nature of companion animal health problems.
Modern veterinary medicine has access to increasingly effective tools for diagnosis and treatment; yet many animals still continue to suffer from disorders that might be reduced or avoided if risks were better understood and measured.
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VetCompass eClinical Trials
People: Camilla Pegram, Dan O'Neill, David Brodbelt, Ruby Chang, Noel Kennedy
Vet Compass Project Type: Dog
The study aims to develop innovative statistical approaches to veterinary electronic patient records to evaluate the effectiveness of clinical interventions in dogs.
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VetCompass eClinical Trials (VETs) – Generating Interventional Evidence from Observational Data
The study aims to develop innovative statistical approaches to veterinary electronic patient records to evaluate the effectiveness of clinical interventions in dogs.
This project aims to develop and apply novel causal inference methods that evaluate real world interventions via routinely collected veterinary EPRs. These methods will be applied to VetCompass data to provide real world inference for some key interventions.