Exploring the Automatisation of Animal Health Surveillance Through Natural Language Processing

Abstract

The Animal and Plant Health Agency (APHA) conducts post-mortem examinations (PMEs) of farm animal species as part of routine scanning surveillance for new and re-emerging diseases that may pose a threat to animal and public health. This paper investigates whether relevant veterinary medical terms can be automatically identified in the free-text summaries entered by Veterinary Investigation Officers (VIOs) on the PME reports. Two natural language processing tasks were performed: (1) named entity recognition, where terms within the free-text were mapped to concepts in the Unified Medical Language System (UMLS) Metathesaurus; and (2) semantic similarity and relatedness also using UMLS. For this pilot study, we focused on two diagnostic codes: salmonellosis (S. Dublin) and Pneumonia NOS (Not Otherwise Specified). The outputs were manually evaluated by VIOs. The results highlight the potential value of natural language processing to identify key concepts and pertinent veterinary medical terms that can be used for scanning surveillance purposes using large, free-text data. We also discuss issues resulting from the inherent bias of UMLS to human medical terms and its use in animal health monitoring.