Autonomic Signal Integration
Signals from multiple autonomic tests are organized into meaningful physiologic variables, characteristic failure patterns, and consistent phenotypes that support structured integration.
AIE™ Overview
The Autonomic Interpretation Engine (AIE™)
The problem
Autonomic testing generates rich physiologic data, yet interpretation remains fragmented, inconsistent, and dependent on individual clinician synthesis. Tests are often read in isolation, limiting mechanistic insight and real-world relevance.
What AIE™ is
The Autonomic Interpretation Engine (AIE™) is a mechanism-based framework that organizes autonomic test data into coherent, physiologically informed summaries that support clinical reasoning without replacing clinicians, tests, or judgment.
What makes AIE™ different
• Multidomain integration of autonomic test data
• Mechanism-first interpretation rather than label assignment
• Explicit distinction between compensation and failure
• Synthesis of mechanistic autonomic phenotypes
Boundaries
• Not a diagnostic device
• Not a treatment or recommendation system
• Not a black-box algorithm
• Not a replacement for clinician judgment
Why this matters
AIE™ aims to reduce variability, improve explanatory clarity, and support mechanism-aligned understanding of autonomic physiology, while enabling future computational and data-driven synthesis.
Status
• Conceptual framework finalized
• Public white paper released (Version 1.1)
• Proprietary implementation maintained in-house
• External validation and pilot collaborations planned
DOI Technical White Paper
This technical white paper defines the conceptual framework and mechanistic boundaries underlying the Autonomic Interpretation Engine (AIE™).
Saeed, K. (2026). A Unified Mechanistic Framework for Multidomain Autonomic Physiology (v1.1, January 2026). Zenodo. DOI: 10.5281/zenodo.18417842
Autonomic Interpretation—Signal to Summary
