Composite Certainty: Addressing Metric Degeneracy in Parameter Inference for Model-Based Diagnostics.

Area of Science:

• Computational modeling
• Biomedical engineering
• Statistical inference

Background:

• Quantifying uncertainty in model parameters is crucial for diagnostic accuracy.
• Existing scalar metrics for uncertainty (e.g., credible interval width) fail to capture the full complexity of parameter distributions.
• This limitation obscures whether parameters are truly well-estimated or simply constrained within a broad range.

Purpose of the Study:

• To develop a novel Composite Certainty Framework (CCF) to address the degeneracy of existing uncertainty metrics.
• To create a robust, unitless consensus score reflecting diagnostic certainty.
• To establish actionable clinical thresholds for measurement noise and simulation budgets.

Main Methods:

• Aggregated five complementary uncertainty metrics: interquartile range, standard deviation, full width at half maximum, Shannon entropy, and mass width.
• Employed non-parametric Borda rank voting to combine individual metric scores into a single consensus certainty score.
• Applied the CCF to a simulation-based inference pipeline for a finite-element model of the human middle ear.

Main Results:

• The CCF revealed parameter-specific identifiability profiles that were not discernible with individual metrics.
• Identified two critical clinical thresholds: maximum tolerable measurement noise and minimum simulation budget for posterior convergence.
• Demonstrated that aggregating diverse metrics provides a more accurate reflection of diagnostic certainty than any single metric.

Conclusions:

• The Composite Certainty Framework offers a superior method for quantifying diagnostic dispersion in uncertainty-aware modeling.
• The framework's consensus score provides deeper insights into parameter identifiability and clinical relevance.
• The CCF is generalizable to various model-based diagnostic pipelines where posterior distribution impacts clinical certainty.