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

By Sweta Karlekar, Tong Niu, Mohit Bansal. 2018

120 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Molecular Biology

Citations in the corpus

• A Survey of the State of Explainable AI for Natural Language Processing (2020, unknown): “As suggested by its name and definition, first-derivative saliency can be used to enable feature importance explainability, especially on word/token-level features (Aubakirova and Bansal, 2016; Karlekar et al., 2018).”
• Advancing Alzheimer Disease Prediction With Large Language Model–Based Linguistic Feature Analysis: Development and Validation Study (2026, unknown): “[54,57,58] Readability encompasses syntactic complexity, lexical diversity, and discourse coherence, all of which are essential for evaluating the organization and comprehensibility of speech.”