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The analysis of disease biomarker data using a mixed hidden Markov model (Open Access publication)

By Johann C Detilleux


A mixed hidden Markov model (HMM) was developed for predicting breeding values of a biomarker (here, somatic cell score) and the individual probabilities of health and disease (here, mastitis) based upon the measurements of the biomarker. At a first level, the unobserved disease process (Markov model) was introduced and at a second level, the measurement process was modeled, making the link between the unobserved disease states and the observed biomarker values. This hierarchical formulation allows joint estimation of the parameters of both processes. The flexibility of this approach is illustrated on the simulated data. Firstly, lactation curves for the biomarker were generated based upon published parameters (mean, variance, and probabilities of infection) for cows with known clinical conditions (health or mastitis due to Escherichia coli or Staphylococcus aureus). Next, estimation of the parameters was performed via Gibbs sampling, assuming the health status was unknown. Results from the simulations and mathematics show that the mixed HMM is appropriate to estimate the quantities of interest although the accuracy of the estimates is moderate when the prevalence of the disease is low. The paper ends with some indications for further developments of the methodology

Topics: Research
Publisher: BioMed Central
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Provided by: PubMed Central
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    1. (2001). A sampling method for estimating the accuracy of predicted breeding values in genetic evaluation,
    2. (2002). An interactive spreadsheet for teaching the forward-Backward algorithm, in:
    3. (2000). Application of a mixed normal mixture model for the estimation of mastitis-related parameters,
    4. (2005). Application of finite mixture model to somatic cell scores of Italian goats,
    5. (2004). Associations between pathogen-specific cases of clinical mastitis and somatic cell count patterns,
    6. (2001). Bacteremia associated with naturally occurring coliform mastitis in dairy cows,
    7. (2001). Bovine mastitis in Finland
    8. (1998). Clinical mastitis in dairy cattle in Ontario: frequency of occurrence and bacteriological isolates,
    9. (2003). Detection of mastitis in dairy cattle by use of mixture models for repeated somatic cell scores: a Bayesian approach via Gibbs sampling,
    10. (2003). Evaluation of immune responses of cattle as a means to identify high and low responders and use of a human microarray to differentiate gene expression,
    11. (2000). Evolutionary dynamics of pathogen resistance and tolerance,
    12. Genetic factors affecting susceptibility to udder pathogens,
    13. (2004). Genetic parameters for clinical mastitis, somatic cell score, and production in the first three lactations of Swedish Holstein cows,
    14. (2007). Mixed hidden Markov model: an extension of the hidden Markov model to the longitudinal data setting,
    15. (1999). Monitoring epidemiologic surveillance data using hidden Markov models,
    16. (2005). Prediction of random effects in finite mixture models with Gaussian components,
    17. (2007). Subclinical mastitis in dairy cows in Swiss organic and conventional production systems,
    18. (2007). Survey of the incidence and aetiology of mastitis on dairy farms in England and Wales,
    19. (2004). The analysis of hospital infection data using hidden Markov models,
    20. (2002). The effect of pathogen-specific clinical mastitis on the lactation curve for somatic cell count,
    21. (2005). The evolution of host resistance: tolerance and control as distinct strategies,

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