Patient specific modelling in diagnosing depression

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

Resumé

Depression is a very common disease. Approximately 10% of people in the Western world experience severe depression during their lifetime and many more experience a mild form of depression. It is commonly believed that depression is caused by malfunctions in the biological system constituted
by the hypothalamus-pituitary-adrenal (HPA) axis. We pose a novel model capable of showing both circardian as well as ultradian oscillations of hormone concentrations. We show that these patterns imitate those observed in the corresponding data. We demonstrate that patient-specific modelling shows
its ability to make diagnoses more precise and to offer individual treatment plans and drug design. Efficient and reliable methods for parameter estimation are crucial. Presently we are investigating how well the Metropolis-Hastings Algorithm of the Bayesian Markov Chain Monte Carlo (MCMC) method for estimating the parameters is working and we are about to do the same using iteratively refined
principal differential analysis (iPDA) or the approximated maximum likelihood estimate (AMLE). Preliminary results for both methods are promising. The next step is to investigate which parameters there are responsible for which pathologies by statistical hypothesis testing.
OriginalsprogEngelsk
BogserieI F A C Workshop Series
Antal sider5
ISSN1474-6670
StatusAfsendt - 2015

Citer dette

Ottesen, J. T. (2015). Patient specific modelling in diagnosing depression. Manuskript afsendt til publicering.
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Patient specific modelling in diagnosing depression. / Ottesen, Johnny T.

I: I F A C Workshop Series, 2015.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

TY - JOUR

T1 - Patient specific modelling in diagnosing depression

AU - Ottesen, Johnny T.

PY - 2015

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N2 - Depression is a very common disease. Approximately 10% of people in the Western world experience severe depression during their lifetime and many more experience a mild form of depression. It is commonly believed that depression is caused by malfunctions in the biological system constitutedby the hypothalamus-pituitary-adrenal (HPA) axis. We pose a novel model capable of showing both circardian as well as ultradian oscillations of hormone concentrations. We show that these patterns imitate those observed in the corresponding data. We demonstrate that patient-specific modelling showsits ability to make diagnoses more precise and to offer individual treatment plans and drug design. Efficient and reliable methods for parameter estimation are crucial. Presently we are investigating how well the Metropolis-Hastings Algorithm of the Bayesian Markov Chain Monte Carlo (MCMC) method for estimating the parameters is working and we are about to do the same using iteratively refinedprincipal differential analysis (iPDA) or the approximated maximum likelihood estimate (AMLE). Preliminary results for both methods are promising. The next step is to investigate which parameters there are responsible for which pathologies by statistical hypothesis testing.

AB - Depression is a very common disease. Approximately 10% of people in the Western world experience severe depression during their lifetime and many more experience a mild form of depression. It is commonly believed that depression is caused by malfunctions in the biological system constitutedby the hypothalamus-pituitary-adrenal (HPA) axis. We pose a novel model capable of showing both circardian as well as ultradian oscillations of hormone concentrations. We show that these patterns imitate those observed in the corresponding data. We demonstrate that patient-specific modelling showsits ability to make diagnoses more precise and to offer individual treatment plans and drug design. Efficient and reliable methods for parameter estimation are crucial. Presently we are investigating how well the Metropolis-Hastings Algorithm of the Bayesian Markov Chain Monte Carlo (MCMC) method for estimating the parameters is working and we are about to do the same using iteratively refinedprincipal differential analysis (iPDA) or the approximated maximum likelihood estimate (AMLE). Preliminary results for both methods are promising. The next step is to investigate which parameters there are responsible for which pathologies by statistical hypothesis testing.

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JO - I F A C Workshop Series

JF - I F A C Workshop Series

SN - 1474-6670

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