Decision tree machine learning applied to bovine tuberculosis risk factors to aid disease control decision making

Romero, Miguel; Chang, Yu‐Mei; Brunton, Lucy A.; Parry, Jessica; Prosser, Alison; Upton, Paul; Rees, Eleanor M.; Tearne, Oliver; Arnold, Mark; Stevens, Kim; Drewe, Julian Ashley

doi:10.1016/j.prevetmed.2019.104860

Cited by 26 publications

(9 citation statements)

References 34 publications

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“…In human medicine, decision tree algorithms such as classification and regression tree (CART) analysis have been used to improve disease screening and diagnosis (Garzotto et al, 2005; Hong et al, 2011), determine patient prognosis (Augustin et al, 2009; Barlin et al, 2013; Hess et al, 1999; Valera et al, 2007), and stratify patients into clinical groups based on their risk of infection or mortality (Fonarow et al, 2005; Hess et al, 1999; Shi et al, 2012; Takahashi et al, 2006). There have been similar applications of decision tree machine learning in veterinary medicine and wildlife conservation, with CART analysis being used to identify prognostic factors of acute colitis in horses (Petersen et al, 2016), evaluate risk factors of bovine tuberculosis (Romero et al, 2020), and estimate marine mammal, sea turtle, and seabird bycatch in gill nets (Carretta et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Sink or swim: Risk stratification of preweaning mortality in harbor seal pups (Phoca vitulina richardii) admitted for rehabilitation

Cole

Fraser

2021

Marine Mammal Science

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To date, few consistent relationships between survival in rehabilitation programs and diagnostic measures recorded upon admission have been identified for harbor seal pups. Veterinary records for 718 unweaned Pacific harbor seal pups (Phoca vitulina richardii) admitted to a rehabilitation center were examined to identify clinical factors associated with preweaning survival and develop a triage tool to stratify pups according to their risk of mortality. Physical, serum chemical, and hematological variables were examined and their relationship with survival to weaning was assessed by logistic regression and classification and regression tree (CART) analysis. Survival to weaning was 85.1% and many clinical variables reflecting the pups’ age, size, growth, injuries, and blood parameters were associated with the likelihood of survival. A decision tree model, consisting of serum concentrations of phosphorus, sodium, and calcium, successfully stratified harbor seal pups into clinical subgroups according to their preweaning mortality risk. For both the derivation and validation cohorts, pups classified as “high risk” had significantly lower odds of survival, while those classified as “low risk” had significantly greater odds of survival. This simple decision tree could serve as a practical triage tool to help identify and direct care towards pups at higher risk of preweaning mortality.

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Section: Introductionmentioning

confidence: 99%

Sink or swim: Risk stratification of preweaning mortality in harbor seal pups (Phoca vitulina richardii) admitted for rehabilitation

Cole

Fraser

2021

Marine Mammal Science

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“…Computerized reasoning applications in indicative radiology might have the option to give precise methods for recognizing the infections for low pay countries. Romero et al ( 2020 ) performed the classification tree analysis to reveal the associations between predictors of tuberculosis in England. They worked on the American Public Health Association data ranging from demographic herd properties and tuberculosis variables using Sam Tuberculosis management.…”

Section: Reported Workmentioning

confidence: 99%

Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda

Kumar

Koul

Singla³

et al. 2022

J Ambient Intell Human Comput

423

173

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Artificial intelligence can assist providers in a variety of patient care and intelligent health systems. Artificial intelligence techniques ranging from machine learning to deep learning are prevalent in healthcare for disease diagnosis, drug discovery, and patient risk identification. Numerous medical data sources are required to perfectly diagnose diseases using artificial intelligence techniques, such as ultrasound, magnetic resonance imaging, mammography, genomics, computed tomography scan, etc. Furthermore, artificial intelligence primarily enhanced the infirmary experience and sped up preparing patients to continue their rehabilitation at home. This article covers the comprehensive survey based on artificial intelligence techniques to diagnose numerous diseases such as Alzheimer, cancer, diabetes, chronic heart disease, tuberculosis, stroke and cerebrovascular, hypertension, skin, and liver disease. We conducted an extensive survey including the used medical imaging dataset and their feature extraction and classification process for predictions. Preferred reporting items for systematic reviews and Meta-Analysis guidelines are used to select the articles published up to October 2020 on the Web of Science, Scopus, Google Scholar, PubMed, Excerpta Medical Database, and Psychology Information for early prediction of distinct kinds of diseases using artificial intelligence-based techniques. Based on the study of different articles on disease diagnosis, the results are also compared using various quality parameters such as prediction rate, accuracy, sensitivity, specificity, the area under curve precision, recall, and F1-score.

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“…A classification tree may also be used to improve an understanding of interconnected and high-risk groups and their likelihood of contracting disease. Romero et al, 2020 [ 46 ] evaluated potential herd-level predictors of bovine tuberculosis using decision trees and multivariable logistic regression in high, edge, and low-risk areas in England. This dataset contained information regarding demographic characteristics of the herd, the history of bTB, cattle movements, badger density, and land class.…”

Section: Contact-based Zoonosesmentioning

confidence: 99%

Artificial Intelligence Models for Zoonotic Pathogens: A Survey

2022

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Zoonotic diseases or zoonoses are infections due to the natural transmission of pathogens between species (animals and humans). More than 70% of emerging infectious diseases are attributed to animal origin. Artificial Intelligence (AI) models have been used for studying zoonotic pathogens and the factors that contribute to their spread. The aim of this literature survey is to synthesize and analyze machine learning, and deep learning approaches applied to study zoonotic diseases to understand predictive models to help researchers identify the risk factors, and develop mitigation strategies. Based on our survey findings, machine learning and deep learning are commonly used for the prediction of both foodborne and zoonotic pathogens as well as the factors associated with the presence of the pathogens.

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Decision tree machine learning applied to bovine tuberculosis risk factors to aid disease control decision making

Cited by 26 publications

References 34 publications

Sink or swim: Risk stratification of preweaning mortality in harbor seal pups (Phoca vitulina richardii) admitted for rehabilitation

Sink or swim: Risk stratification of preweaning mortality in harbor seal pups (Phoca vitulina richardii) admitted for rehabilitation

Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda

Artificial Intelligence Models for Zoonotic Pathogens: A Survey

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