### Abstract

Original language | English |
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Title of host publication | Prediction Methods for Blood Glucose Concentration : Design, Use and Evaluation |

Editors | Harald Kirchsteiger, John Bagterp Jørgensen, Eric Renard, Luigi del Re |

Publisher | Springer |

Publication date | 2016 |

Pages | 183-209 |

ISBN (Print) | 978-3-319-25911-6 |

ISBN (Electronic) | 978-3-319-25913-0 |

DOIs | |

Publication status | Published - 2016 |

Series | Lecture Notes in Bioengineering |
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ISSN | 2195-271X |

### Cite this

*Prediction Methods for Blood Glucose Concentration: Design, Use and Evaluation*(pp. 183-209). Springer. Lecture Notes in Bioengineering https://doi.org/10.1007/978-3-319-25913-0_10

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*Prediction Methods for Blood Glucose Concentration: Design, Use and Evaluation.*Springer, Lecture Notes in Bioengineering, pp. 183-209. https://doi.org/10.1007/978-3-319-25913-0_10

**Modeling and Prediction Using Stochastic Differential Equations.** / Juhl, Rune; Møller, Jan Kloppenborg; Jørgensen, John Bagterp; Madsen, Henrik.

Research output: Chapter in Book/Report/Conference proceeding › Book chapter › Research › peer-review

TY - CHAP

T1 - Modeling and Prediction Using Stochastic Differential Equations

AU - Juhl, Rune

AU - Møller, Jan Kloppenborg

AU - Jørgensen, John Bagterp

AU - Madsen, Henrik

PY - 2016

Y1 - 2016

N2 - Pharmacokinetic/pharmakodynamic (PK/PD) modeling for a single subject is most often performed using nonlinear models based on deterministic ordinary differential equations (ODEs), and the variation between subjects in a population of subjects is described using a population (mixed effects) setup that describes the variation between subjects. The ODE setup implies that the variation for a single subject is described by a single parameter (or vector), namely the variance (covariance) of the residuals. Furthermore the prediction of the states is given as the solution to the ODEs and hence assumed deterministic and can predict the future perfectly. A more realistic approach would be to allow for randomness in the model due to e.g., the model be too simple or errors in input. We describe a modeling and prediction setup which better reflects reality and suggests stochastic differential equations (SDEs) for modeling and forecasting. It is argued that this gives models and predictions which better reflect reality. The SDE approach also offers a more adequate framework for modeling and a number of efficient tools for model building. A software package (CTSM-R) for SDE-based modeling is briefly described.

AB - Pharmacokinetic/pharmakodynamic (PK/PD) modeling for a single subject is most often performed using nonlinear models based on deterministic ordinary differential equations (ODEs), and the variation between subjects in a population of subjects is described using a population (mixed effects) setup that describes the variation between subjects. The ODE setup implies that the variation for a single subject is described by a single parameter (or vector), namely the variance (covariance) of the residuals. Furthermore the prediction of the states is given as the solution to the ODEs and hence assumed deterministic and can predict the future perfectly. A more realistic approach would be to allow for randomness in the model due to e.g., the model be too simple or errors in input. We describe a modeling and prediction setup which better reflects reality and suggests stochastic differential equations (SDEs) for modeling and forecasting. It is argued that this gives models and predictions which better reflect reality. The SDE approach also offers a more adequate framework for modeling and a number of efficient tools for model building. A software package (CTSM-R) for SDE-based modeling is briefly described.

U2 - 10.1007/978-3-319-25913-0_10

DO - 10.1007/978-3-319-25913-0_10

M3 - Book chapter

SN - 978-3-319-25911-6

SP - 183

EP - 209

BT - Prediction Methods for Blood Glucose Concentration

A2 - Kirchsteiger, Harald

A2 - Bagterp Jørgensen, John

A2 - Renard, Eric

A2 - del Re, Luigi

PB - Springer

ER -