Mathematical Model Error Measures for Combined HPA and SA Axes

5.5 KB of error measures from a differential equation model simulating the combined hypothalamic-pituitary-adrenal and sympathoadrenal axes. The model parameters were estimated using constrained optimization against time series data of cortisol and catecholamine dynamics under acute physical stress. The dataset supports analysis of the proposed extension to Bangsgaard and Ottesen's HPA axis model.

Use Cases

• Analyze model error measures to validate the proposed combined HPA and SA axes model against cortisol and catecholamine time series data.
• Use the error measures from subject-specific parameter estimation to assess the model's qualitative agreement with collected acute stress response data.
• Compare error metrics from the Morris screening sensitivity analysis to identify key parameters influencing cortisol and catecholamine dynamics.

Strengths

• Model parameters were estimated using constrained optimization against empirical time series data of cortisol and catecholamines.
• The dataset is derived from a mathematical model that uniquely combines the HPA and SA axes, which previous models did not.
• The model shows qualitative agreement with collected data after subject-specific parameter estimation.

Limitations

• The dataset is very small at 5.5 KB, indicating limited scope and likely a small number of calculated error measures or parameters.
• The underlying time series data for cortisol and catecholamine dynamics is not included, limiting direct validation.
• The model's performance is described only as 'qualitative agreement', lacking quantitative error bounds or statistical significance tests.

Provenance

Source

Helen A. Harris via figshare

Collection Method

Error measures derived from a proposed differential equation-based mathematical model, with parameters estimated via constrained optimization against time series data.

Time Range

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Freshness

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Geography

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