The analysis of disease biomarker data using a mixed hidden Markov model (<it>Open Access publication</it>)
<p>Abstract</p> <p>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 un...
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doaj-3b44301c43aa4dc0aecaed012b8e21322020-11-24T22:18:12ZdeuBMCGenetics Selection Evolution0999-193X1297-96862008-09-0140549150910.1186/1297-9686-40-5-491The analysis of disease biomarker data using a mixed hidden Markov model (<it>Open Access publication</it>)Detilleux Johann C<p>Abstract</p> <p>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 <it>Escherichia coli </it>or <it>Staphylococcus aureus</it>). Next, estimation of the parameters was performed <it>via </it>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.</p> http://www.gsejournal.org/content/40/5/491hidden Markov modelmixed modelmastitissomatic cell score |
collection |
DOAJ |
language |
deu |
format |
Article |
sources |
DOAJ |
author |
Detilleux Johann C |
spellingShingle |
Detilleux Johann C The analysis of disease biomarker data using a mixed hidden Markov model (<it>Open Access publication</it>) Genetics Selection Evolution hidden Markov model mixed model mastitis somatic cell score |
author_facet |
Detilleux Johann C |
author_sort |
Detilleux Johann C |
title |
The analysis of disease biomarker data using a mixed hidden Markov model (<it>Open Access publication</it>) |
title_short |
The analysis of disease biomarker data using a mixed hidden Markov model (<it>Open Access publication</it>) |
title_full |
The analysis of disease biomarker data using a mixed hidden Markov model (<it>Open Access publication</it>) |
title_fullStr |
The analysis of disease biomarker data using a mixed hidden Markov model (<it>Open Access publication</it>) |
title_full_unstemmed |
The analysis of disease biomarker data using a mixed hidden Markov model (<it>Open Access publication</it>) |
title_sort |
analysis of disease biomarker data using a mixed hidden markov model (<it>open access publication</it>) |
publisher |
BMC |
series |
Genetics Selection Evolution |
issn |
0999-193X 1297-9686 |
publishDate |
2008-09-01 |
description |
<p>Abstract</p> <p>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 <it>Escherichia coli </it>or <it>Staphylococcus aureus</it>). Next, estimation of the parameters was performed <it>via </it>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.</p> |
topic |
hidden Markov model mixed model mastitis somatic cell score |
url |
http://www.gsejournal.org/content/40/5/491 |
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