Comprehensive representation of health-related phenotypes in one million dogs using topic modelling of electronic health records

Abstract

Historically, veterinary studies screening for breed, age and sex predisposition to disease have relied on collating small-scale studies of clinical datasets. The availability of larger datasets through groups such as the Small Animal Veterinary Surveillance Network (SAVSNET) promise access to information regarding wide range of clinical presentations at scale, however, methodological limitations surrounding the extraction of specific disease information or screening for disease predispositions result in a substantial reduction in the number of animals studied. These studies often address very focused hypotheses - only leveraging a small fraction of the intrinsic value of the data at any one time. Here, we implemented an unsupervised machine learning methodology, creating a representation of a large volume of clinical notes collected by SAVSNET from veterinary practices across the UK. We capture breed, age and sex predisposition and offer statistical and temporal possibilities across various clinically important presentations. We utilise BERTopic, a topic-modelling tool based on Bidirectional Encoder Representations using Transformers (BERT) architecture, which surfaces known phenotypes, such as breed predispositions to hypoadrenocorticism, diabetes mellitus and mitral valve disease, and potential novel patterns of disease phenotypes. This scalable and granular modelling technique facilitates the rapid interrogation of large clinical datasets, enabling the identification of broad phenotypic diversity within the population and the early detection of temporal changes indicative of emerging infectious or environmental diseases.