ArticleFrontiers in public health2022
A Transfer Learning Method for Detecting Alzheimer's Disease Based on Speech and Natural Language Processing.
Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed.
- Cross-Lingual Alzheimer's Disease Speech Detection: Polarity Inversion and Few-Shot Calibration Strategies.Bioengineering (Basel, Switzerland) · 2026Article
- NLP-ROPCare: predicting retinopathy of prematurity with admission notes using natural language processing.BMJ open ophthalmology · 2026Observational
- Detection of brain network abnormalities by graph invariants in Alzheimer's disease using MRI images.Scientific reports · 2025Article
- Automated Speech Markers of Alzheimer Dementia: Test of Cross-Linguistic Generalizability.Journal of medical Internet research · 2025Article
- NeuroNet-AD: A Multimodal Deep Learning Framework for Multiclass Alzheimer's Disease Diagnosis.Bioengineering (Basel, Switzerland) · 2025Article
- Usefulness of Generative Artificial Intelligence (AI) Tools in Pediatric Dentistry.Diagnostics (Basel, Switzerland) · 2024Article
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- Artificial General Intelligence for the Detection of Neurodegenerative Disorders.Sensors (Basel, Switzerland) · 2024Review
- Natural language processing of clinical notes enables early inborn error of immunity risk ascertainment.The journal of allergy and clinical immunology. Global · 2024Article
- AI and narrative embeddings detect PTSD following childbirth via birth stories.Scientific reports · 2024Article
- Understanding New Machine Learning Architectures: Practical Generative Artificial Intelligence for Anesthesiologists.Anesthesiology · 2024Review
- OpenAI's Narrative Embeddings Can Be Used for Detecting Post-Traumatic Stress Following Childbirth Via Birth Stories.Research square · 2024Article
- Direct Clinical Applications of Natural Language Processing in Common Neurological Disorders: Scoping Review.JMIR neurotechnology · 2024Review
- Understanding mental health through computers: An introduction to computational psychiatry.Frontiers in psychiatry · 2023Review
- Learning implicit sentiments in Alzheimer's disease recognition with contextual attention features.Frontiers in aging neuroscience · 2023Article
- Characterizing and detecting delirium with clinical and computational measures of speech and language disturbance.Journal of psychiatry & neuroscience : JPNArticle
- Natural language processing in Alzheimer's disease research: Systematic review of methods, data, and efficacy.Alzheimer's & dementia (Amsterdam, Netherlands)Review
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4 authors.
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Abstract
Alzheimer's disease (AD) is a neurodegenerative disease that is difficult to be detected using convenient and reliable methods. The language change in patients with AD is an important signal of their cognitive status, which potentially helps in early diagnosis. In this study, we developed a transfer learning model based on speech and natural language processing (NLP) technology for the early diagnosis of AD. The lack of large datasets limits the use of complex neural network models without feature engineering, while transfer learning can effectively solve this problem. The transfer learning model is firstly pre-trained on large text datasets to get the pre-trained language model, and then, based on such a model, an AD classification model is performed on small training sets. Concretely, a distilled bidirectional encoder representation (distilBert) embedding, combined with a logistic regression classifier, is used to distinguish AD from normal controls. The model experiment was evaluated on Alzheimer's dementia recognition through spontaneous speech datasets in 2020, including the balanced 78 healthy controls (HC) and 78 patients with AD. The accuracy of the proposed model is 0.88, which is almost equivalent to the champion score in the challenge and a considerable improvement over the baseline of 75% established by organizers of the challenge. As a result, the transfer learning method in this study improves AD prediction, which does not only reduces the need for feature engineering but also addresses the lack of sufficiently large datasets.
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