ArticlePloS one2018
Deep language space neural network for classifying mild cognitive impairment and Alzheimer-type dementia.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 21 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Neural Computation-Based Methods for the Early Diagnosis and Prognosis of Alzheimer's Disease Not Using Neuroimaging Biomarkers: A Systematic Review.Journal of Alzheimer's disease : JAD · 2024Pooled it
- Design of an artificial intelligence model to screen spontaneous speech to detect Alzheimer's Disease.PLOS digital health · 2026Article
- Deep ensemble learning with transformer models for enhanced Alzheimer's disease detection.Scientific reports · 2025Article
- A Systematic Review of Natural Language Processing Techniques for Early Detection of Cognitive Impairment.Mayo Clinic proceedings. Digital health · 2025Review
- Bigger But Not Better: Small Neural Language Models Outperform Large Language Models in Detection of Thought Disorder.... Workshop on Computational Linguistics and Clinical Psychology · 2025Article
- Artificial intelligence technologies for enhancing neurofunctionalities: a comprehensive review with applications in Alzheimer's disease research.Frontiers in aging neuroscience · 2025Review
- Combining artificial neural networks and a marginal structural model to predict the progression from depression to Alzheimer's disease.Frontiers in dementia · 2024Article
- Speech and language markers of neurodegeneration: a call for global equity.Brain : a journal of neurology · 2023Review
- Learning implicit sentiments in Alzheimer's disease recognition with contextual attention features.Frontiers in aging neuroscience · 2023Article
- An approach for assisting diagnosis of Alzheimer's disease based on natural language processing.Frontiers in aging neuroscience · 2023Article
- Deep learning-based speech analysis for Alzheimer's disease detection: a literature review.Alzheimer's research & therapy · 2022Review
- Deep Learning-Based Diagnosis of Alzheimer's Disease.Journal of personalized medicine · 2022Review
- Automated text-level semantic markers of Alzheimer's disease.Alzheimer's & dementia (Amsterdam, Netherlands) · 2022Article
- An efficient context-aware screening system for Alzheimer's disease based on neuropsychology test.Scientific reports · 2021Article
- Crossing the 'Cookie Theft' Corpus Chasm: Applying what BERT Learns from Outside Data to the ADReSS Challenge Dementia Detection Task.Frontiers in computer science · 2021Article
- Improving Alzheimer's Disease Detection for Speech Based on Feature Purification Network.Frontiers in public health · 2021Article
- A machine learning-based linguistic battery for diagnosing mild cognitive impairment due to Alzheimer's disease.PloS one · 2020Article
- Analyze Informant-Based Questionnaire for The Early Diagnosis of Senile Dementia Using Deep Learning.IEEE journal of translational engineering in health and medicine · 2020Article
- Correction: Deep language space neural network for classifying mild cognitive impairment and Alzheimer-type dementia.PloS one · 2019Article
- An explainable machine learning model of cognitive decline derived from speech.Alzheimer's & dementia (Amsterdam, Netherlands)Article
Corrections and comments
- Erratum issued
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
It has been quite a challenge to diagnose Mild Cognitive Impairment due to Alzheimer's disease (MCI) and Alzheimer-type dementia (AD-type dementia) using the currently available clinical diagnostic criteria and neuropsychological examinations. As such we propose an automated diagnostic technique using a variant of deep neural networks language models (DNNLM) on the verbal utterances of affected individuals. Motivated by the success of DNNLM on natural language tasks, we propose a combination of deep neural network and deep language models (D2NNLM) for classifying the disease. Results on the DementiaBank language transcript clinical dataset show that D2NNLM sufficiently learned several linguistic biomarkers in the form of higher order n-grams to distinguish the affected group from the healthy group with reasonable accuracy on very sparse clinical datasets.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.