Linguistic Features Identify Alzheimer’s Disease in Narrative Speech

By Kathleen C. Fraser, Jed A. Meltzer, Frank Rudzicz. Journal of Alzheimer's Disease, 2015

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How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Secure and Robust Machine Learning for Healthcare: A Survey (2020, background): “Alzheimer’s disease identification using linguistic features is presented in [82].”
On the importance of normative data in speech-based assessment (2017, background): “Interestingly, many lexical features (indicative of previous work that only used the DB data set [6]) ceased to be important when including the normative data.”
Alzheimer's Dementia Recognition through Spontaneous Speech: The ADReSS Challenge (2020, background): “92%, 80% and 79% and 64% have been reported in the literature [3, 22, 23, 7].”
Artificial intelligence in clinical and genomic diagnostics (2019, background): “…health data such as those provided by an electrocardiogram [2], speech-recognition techniques can be used for detection of neurological disorders [3], and AI-based natural language processing can be helpful in the extraction of meaningful information from electronic health record (EHR) data [4].”
Advancing Alzheimer Disease Prediction With Large Language Model–Based Linguistic Feature Analysis: Development and Validation Study (2026, unknown): “This LLM-driven approach provides a scalable and interpretable method for analyzing speech, aligning with recent advancements in biomedical informatics for dementia diagnosis [55,56,59].”