mlsanityData to 14 Oct 2026

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

arXiv 1804.06440 ↗First posted 17 Apr 2018cs.CLcited by 19

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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.

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

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Citing papers per year: 2021: 4; 2022: 2; 2023: 7; 2024: 3; 2025: 3.

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  1. 2023Two Directions for Clinical Data Generation with Large Language Models: Data-to-Label and Label-to-Datacited by 26
  2. 2021Explainable Identification of Dementia from Transcripts using Transformer Networkscited by 19
  3. 2023Context-aware attention layers coupled with optimal transport domain adaptation and multimodal fusion methods for recognizing dementia from spontaneous speechcited by 5
  4. 2023Reformulating NLP tasks to Capture Longitudinal Manifestation of Language Disorders in People with Dementiacited by 4
  5. 2024A Systematic Review of NLP for Dementia -- Tasks, Datasets and Opportunitiescited by 4
  6. 2023Classification of Alzheimers Disease with Deep Learning on Eye-tracking Datacited by 3
  7. 2021A Longitudinal Multi-modal Dataset for Dementia Monitoring and Diagnosiscited by 2
  8. 2023Tell Model Where to Attend: Improving Interpretability of Aspect-Based Sentiment Classification via Small Explanation Annotationscited by 2
  9. 2024Profiling Patient Transcript Using Large Language Model Reasoning Augmentation for Alzheimer's Disease Detectioncited by 2
  10. 2025Dementia Through Different Eyes: Explainable Modeling of Human and LLM Perceptions for Early Awarenesscited by 2
  11. 2021Detecting Autism Spectrum Disorders with Machine Learning Models Using Speech Transcriptscited by 1
  12. 2022AI-powered Language Assessment Tools for Dementiacited by 1
7 more
  1. 2024Learning Intrinsic Dimension via Information Bottleneck for Explainable Aspect-based Sentiment Analysiscited by 1
  2. 2025Developing Conversational Speech Systems for Robots to Detect Speech Biomarkers of Cognition in People Living with Dementiacited by 1
  3. 2021ML-Based Analysis to Identify Speech Features Relevant in Predicting Alzheimer's Diseasecited by 0
  4. 2022NeuraHealth: An Automated Screening Pipeline to Detect Undiagnosed Cognitive Impairment in Electronic Health Records with Deep Learning and Natural Language Processingcited by 0
  5. 2023Improving Prediction Performance and Model Interpretability through Attention Mechanisms from Basic and Applied Research Perspectivescited by 0
  6. 2023AI and Non AI Assessments for Dementiacited by 0
  7. 2025A Robust Classification Method using Hybrid Word Embedding for Early Diagnosis of Alzheimer's Diseasecited by 0