Year: 2026
Type: article
Abstract: Event-Based Models (EBMs) are widely used to infer latent disease progression from cross-sectional biomarker data, with existing studies employing diverse approaches to characterize uncertainty, stability, and validation. However, comparatively little guidance exists for systematically integrating and reporting these complementary forms of diagnostic evidence. We develop a Reporting Framework for Event-Based Model Diagnostics supported by a theoretical decomposition and empirical characterization of local ordering uncertainty in classical single-order EBMs. Our swap-local decomposition of the Evidence Lower Bound (ELBO) separates each local ordering decision into a posterior visibility term reflecting stage overlap and a subject-level contrast term reflecting directional evidence, enabling low-information ambiguity to be distinguished from genuine competing cohort evidence. We characterize these diagnostics through controlled synthetic experiments and an Alzheimer's disease cerebrospinal fluid proteomic case study, and evaluate complementary uncertainty targets through local optimization perturbations, leave-two-out sensitivity analyses, and repeated full-pipeline ReduXis experiments. The latter assess end-to-end initialization sensitivity and are distinct from validation of the classical fixed-fit swap-local decomposition. Collectively, these theoretical and empirical findings motivate a practical reporting framework that distinguishes broadly applicable core reporting elements from conditional, model-specific diagnostics. By organizing complementary diagnostic evidence according to its uncertainty target and model-specific scope, this work provides a principled foundation for more transparent, interpretable, and reproducible reporting of Event-Based Models.
Source: Frontiers in Neuroinformatics
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,801
OpenAlex ID: W7225834533