ArticleBMC veterinary research2012
Identifying associations between pig pathologies using a multi-dimensional machine learning methodology.
Article in BMC veterinary research, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Weaning performance prediction in lactating sows using machine learning, for precision nutrition and intelligent feeding.Animal nutrition (Zhongguo xu mu shou yi xue hui) · 2025Article
- Applications and Considerations of Artificial Intelligence in Veterinary Sciences: A Narrative Review.Veterinary medicine and science · 2025Review
- Building composite indices in the age of big data - Application to honey bee exposure to infectious and parasitic agents.Heliyon · 2023Article
- Baicalin Alleviate Apoptosis via PKC-MAPK Pathway in Porcine Peritoneal Mesothelial Cells Induced byMolecules (Basel, Switzerland) · 2022Article
- Welfare of pigs on farm.EFSA journal. European Food Safety Authority · 2022Article
- Modelling the links between farm characteristics, respiratory health and pig production traits.Scientific reports · 2021Article
- Abattoir-Based Measures to Assess Swine Welfare: Analysis of the Methods Adopted in European Slaughterhouses.Animals : an open access journal from MDPI · 2021Review
- The Evidence for a Causal Link Between Disease and Damaging Behavior in Pigs.Frontiers in veterinary science · 2021Review
- Cost-Effectiveness Analysis of Seven Measures to Reduce Tail Biting Lesions in Fattening Pigs.Frontiers in veterinary science · 2021Article
- Scoring pleurisy in slaughtered pigs using convolutional neural networks.Veterinary research · 2020Article
- Influence of temperature on prevalence of health and welfare conditions in pigs: time-series analysis of pig abattoir inspection data in England and Wales.Epidemiology and infection · 2020Article
- Importance of the knowledge of pathological processes for risk-based inspection in pig slaughterhouses (Study of 2002 to 2016).Asian-Australasian journal of animal sciences · 2018Article
- Connecting Different Data Sources to Assess the Interconnections between Biosecurity, Health, Welfare, and Performance in Commercial Pig Farms in Great Britain.Frontiers in veterinary science · 2018Article
- Article
- Study on the Association between Tail Lesion Score, Cold Carcass Weight, and Viscera Condemnations in Slaughter Pigs.Frontiers in veterinary science · 2016Article
- Comparison of Respiratory Disease Prevalence among Voluntary Monitoring Systems for Pig Health and Welfare in the UK.PloS one · 2015Article
- A comparison between lesions found during meat inspection of finishing pigs raised under organic/free-range conditions and conventional, indoor conditions.Porcine health management · 2015Article
- Improving epidemiologic data analyses through multivariate regression modelling.Emerging themes in epidemiology · 2013Article
- Using Bayesian networks to explore the role of weather as a potential determinant of disease in pigs.Preventive veterinary medicine · 2013Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAbattoir 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.
resultsMulti-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.
conclusionsThe 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.
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