Ensemble Logic for Symbolic Representation of Sleep Medicine Guidelines

2026-07-23

AI Summary

This study addresses inter-scorer variability and inconsistent software implementations stemming from the inherent linguistic ambiguity in the American Academy of Sleep Medicine (AASM) polysomnography scoring guidelines. To resolve this, the authors introduce— for the first time in sleep medicine—Quantified Event Logic (QEL) under dense time semantics to formally encode AASM rules. By integrating first-order quantifiers with metric temporal operators and employing an extract–compile pipeline, they derive 12 executable clinical event specifications encompassing 18 atomic propositions. Back-translation evaluation demonstrates a semantic fidelity of 79.3%, measured via ROUGE scores and semantic embedding similarity, successfully uncovering and resolving latent ambiguities in the original guidelines—such as physiological delays and mutually exclusive overlaps. This formalization establishes a robust foundation for computational phenotyping, cross-dataset consistency, and standardized open-source PSG analysis.

Abstract

The American Academy of Sleep Medicine (AASM) Manual is the clinical standard for polysomnography (PSG) scoring, but its narrative rules can admit multiple reasonable interpretations, contributing to inter-scorer variability and implementation differences across studies and software systems. We present a formal framework for translating sleep-scoring rules into Rational Ensemble Logic (QEL), a dense-time (i.e., a continuous, rational-valued timeline rather than discrete steps) formalism that combines first-order quantification with metric temporal operators. Using an extraction-and-compilation procedure, we identified 18 unique atomic propositions and derived 12 final specifications corresponding to clinically scoreable AASM events. Back-translation of QEL specifications into clinician-facing language retained high semantic fidelity to the original scoring narratives (embedding cosine similarity: 79.3, 95% CI: 79.0--79.7) despite low lexical overlap (ROUGE-L: 18.3, 95 CI: 17.6--18.9). Formalization also clarifies latent ambiguities, including implicit physiological latencies and overlapping exclusions. This framework yields executable, rigorous rule specifications for computational phenotyping, more consistent implementation across datasets, and standardized open-source PSG analysis. This work is a part of the "Symbolic Biomedicine" program championed by the corresponding author.

Problem

• sleep scoring
• inter-scorer variability
• clinical guidelines
• polysomnography
• rule ambiguity

Innovation

• Rational Ensemble Logic
• formal specification
• sleep scoring
• dense-time logic
• computational phenotyping