# Detecting Linguistic Characteristics of Alzheimer's Dementia by Interpreting Neural Models

arXiv: https://arxiv.org/abs/1804.06440 · HTML: https://mlsanity.com/paper/1804.06440/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2018.

## Built on by (most cited first, 19 of 19)

- [Two Directions for Clinical Data Generation with Large Language Models: Data-to-Label and Label-to-Data](https://mlsanity.com/paper/2401.06774/) (2023)
- [Explainable Identification of Dementia from Transcripts using Transformer Networks](https://mlsanity.com/paper/2109.06980/) (2021)
- [Context-aware attention layers coupled with optimal transport domain adaptation and multimodal fusion methods for recognizing dementia from spontaneous speech](https://mlsanity.com/paper/2305.16406/) (2023)
- [Reformulating NLP tasks to Capture Longitudinal Manifestation of Language Disorders in People with Dementia](https://mlsanity.com/paper/2310.09897/) (2023)
- [A Systematic Review of NLP for Dementia -- Tasks, Datasets and Opportunities](https://mlsanity.com/paper/2409.19737/) (2024)
- [Classification of Alzheimers Disease with Deep Learning on Eye-tracking Data](https://mlsanity.com/paper/2309.12574/) (2023)
- [A Longitudinal Multi-modal Dataset for Dementia Monitoring and Diagnosis](https://mlsanity.com/paper/2109.01537/) (2021)
- [Tell Model Where to Attend: Improving Interpretability of Aspect-Based Sentiment Classification via Small Explanation Annotations](https://mlsanity.com/paper/2302.10479/) (2023)
- [Profiling Patient Transcript Using Large Language Model Reasoning Augmentation for Alzheimer's Disease Detection](https://mlsanity.com/paper/2409.12541/) (2024)
- [Dementia Through Different Eyes: Explainable Modeling of Human and LLM Perceptions for Early Awareness](https://mlsanity.com/paper/2505.13418/) (2025)
- [Detecting Autism Spectrum Disorders with Machine Learning Models Using Speech Transcripts](https://mlsanity.com/paper/2110.03281/) (2021)
- [AI-powered Language Assessment Tools for Dementia](https://mlsanity.com/paper/2209.12652/) (2022)
- [Learning Intrinsic Dimension via Information Bottleneck for Explainable Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2402.18145/) (2024)
- [Developing Conversational Speech Systems for Robots to Detect Speech Biomarkers of Cognition in People Living with Dementia](https://mlsanity.com/paper/2502.10896/) (2025)
- [ML-Based Analysis to Identify Speech Features Relevant in Predicting Alzheimer's Disease](https://mlsanity.com/paper/2110.13023/) (2021)
- [NeuraHealth: An Automated Screening Pipeline to Detect Undiagnosed Cognitive Impairment in Electronic Health Records with Deep Learning and Natural Language Processing](https://mlsanity.com/paper/2202.00478/) (2022)
- [Improving Prediction Performance and Model Interpretability through Attention Mechanisms from Basic and Applied Research Perspectives](https://mlsanity.com/paper/2303.14116/) (2023)
- [AI and Non AI Assessments for Dementia](https://mlsanity.com/paper/2307.01210/) (2023)
- [A Robust Classification Method using Hybrid Word Embedding for Early Diagnosis of Alzheimer's Disease](https://mlsanity.com/paper/2510.14332/) (2025)

## Abstract (arXiv)

Alzheimer's disease (AD) is an irreversible and progressive brain disease that can be stopped or slowed down with medical treatment. Language changes serve as a sign that a patient's cognitive functions have been impacted, potentially leading to early diagnosis. In this work, we use NLP techniques to classify and analyze the linguistic characteristics of AD patients using the DementiaBank dataset. We apply three neural models based on CNNs, LSTM-RNNs, and their combination, to distinguish between language samples from AD and control patients. We achieve a new independent benchmark accuracy for the AD classification task. More importantly, we next interpret what these neural models have learned about the linguistic characteristics of AD patients, via analysis based on activation clustering and first-derivative saliency techniques. We then perform novel automatic pattern discovery inside activation clusters, and consolidate AD patients' distinctive grammar patterns. Additionally, we show that first derivative saliency can not only rediscover previous language patterns of AD patients, but also shed light on the limitations of neural models. Lastly, we also include analysis of gender-separated AD data.