A Neurosymbolic Approach for Explainable Early Diagnosis of Alzheimer's Disease
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.
Sources
- MedGemma Technical Report
- SLOT: Structuring the Output of Large Language Models
- Synthesizing Visual Concepts as Vision-Language Programs
Related papers
- Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
- AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
- Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
- Convergence issues in Relational Concept Analysis based on AOC-posets
- Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
- Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks