Atherosclerosis through Hierarchical Explainable Neural Network Analysis
Journal:
arXiv
Published Date:
Jul 10, 2025
Abstract
In this work, we study the problem pertaining to personalized classification
of subclinical atherosclerosis by developing a hierarchical graph neural
network framework to leverage two characteristic modalities of a patient:
clinical features within the context of the cohort, and molecular data unique
to individual patients. Current graph-based methods for disease classification
detect patient-specific molecular fingerprints, but lack consistency and
comprehension regarding cohort-wide features, which are an essential
requirement for understanding pathogenic phenotypes across diverse
atherosclerotic trajectories. Furthermore, understanding patient subtypes often
considers clinical feature similarity in isolation, without integration of
shared pathogenic interdependencies among patients. To address these
challenges, we introduce ATHENA: Atherosclerosis Through Hierarchical
Explainable Neural Network Analysis, which constructs a novel hierarchical
network representation through integrated modality learning; subsequently, it
optimizes learned patient-specific molecular fingerprints that reflect
individual omics data, enforcing consistency with cohort-wide patterns. With a
primary clinical dataset of 391 patients, we demonstrate that this
heterogeneous alignment of clinical features with molecular interaction
patterns has significantly boosted subclinical atherosclerosis classification
performance across various baselines by up to 13% in area under the receiver
operating curve (AUC) and 20% in F1 score. Taken together, ATHENA enables
mechanistically-informed patient subtype discovery through explainable AI
(XAI)-driven subnetwork clustering; this novel integration framework
strengthens personalized intervention strategies, thereby improving the
prediction of atherosclerotic disease progression and management of their
clinical actionable outcomes.
