A Neurosymbolic Approach for Explainable Early Diagnosis of Alzheimer's Disease

arXiv:2607.29530 · cs.LG · Submitted 2026-08-20 · Read on arXiv

Ranveer Singh, Pranuthi Tenali, Saurabh Mathur, Ameet Soni, Vaishali Phatak, Karla Lynch, Daniel Murman, Matthew Rizzo, Sriraam Natarajan

cs.LG

Submitted: 2026-08-20

Updated: 2026-08-21

Code: https://github.com/s-ranveer/verbal_fluency_alzheimers

License: http://creativecommons.org/licenses/by/4.0/

The gist: Identifying reliable Alzheimer's disease (AD) markers typically requires manual, labor-intensive transcription and expert analysis, limiting its scale.

Terminology

Abstract

Identifying reliable Alzheimer's disease (AD) markers typically requires manual, labor-intensive transcription and expert analysis, limiting its scale. We introduce an automated pipeline that extracts qualitative knowledge about potential AD progression indicators directly from audio recordings of verbal fluency tests. Our method uses pretrained foundation models to process raw audio and extract clinically relevant variables to construct a Bayesian Network (BN); this BN is used to reason about the AD progression markers and infer their qualitative relationships. Our system successfully recovers known clinical knowledge and identifies novel relationships between linguistic markers.

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