Scottish Agricultural College, Kings Buildings, West Mains Road, Edinburgh EH9 3JG, UK.
BMC Vet Res. 2012 Aug 31;8:151. doi: 10.1186/1746-6148-8-151.
Abattoir detected pathologies are of crucial importance to both pig production and food safety. Usually, more than one pathology coexist in a pig herd although it often remains unknown how these different pathologies interrelate to each other. Identification of the associations between different pathologies may facilitate an improved understanding of their underlying biological linkage, and support the veterinarians in encouraging control strategies aimed at reducing the prevalence of not just one, but two or more conditions simultaneously.
Multi-dimensional machine learning methodology was used to identify associations between ten typical pathologies in 6485 batches of slaughtered finishing pigs, assisting the comprehension of their biological association. Pathologies potentially associated with septicaemia (e.g. pericarditis, peritonitis) appear interrelated, suggesting on-going bacterial challenges by pathogens such as Haemophilus parasuis and Streptococcus suis. Furthermore, hepatic scarring appears interrelated with both milk spot livers (Ascaris suum) and bacteria-related pathologies, suggesting a potential multi-pathogen nature for this pathology.
The application of novel multi-dimensional machine learning methodology provided new insights into how typical pig pathologies are potentially interrelated at batch level. The methodology presented is a powerful exploratory tool to generate hypotheses, applicable to a wide range of studies in veterinary research.
屠宰场检测到的病理学对于猪的生产和食品安全都至关重要。通常，尽管猪群中常常存在多种病理学共存，但这些不同的病理学如何相互关联仍不得而知。识别不同病理学之间的关联可以帮助更好地理解它们潜在的生物学联系，并支持兽医鼓励控制策略，旨在减少不仅一种，而是两种或更多疾病同时发生的流行。
多维机器学习方法用于识别 6485 批屠宰肥猪中十种典型病理学之间的关联，有助于理解它们的生物学关联。与败血症（如心包炎、腹膜炎）相关的病理学似乎相互关联，表明副猪嗜血杆菌和猪链球菌等病原体持续存在细菌挑战。此外，肝瘢痕与乳斑肝（蛔虫）和与细菌相关的病理学相关，表明该病理学可能具有潜在的多病原体性质。
新型多维机器学习方法的应用为了解典型猪病理学如何在批次水平上潜在相互关联提供了新的见解。所提出的方法是一种强大的探索性工具，可以为兽医研究的广泛研究产生假设。
