ArticleFrontiers in aging neuroscience2019
Predicting MCI Status From Multimodal Language Data Using Cascaded Classifiers.
Article in Frontiers in aging neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 4 of them syntheses that pooled it.
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Who cites it
40 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Diagnostic utility of speech-based biomarkers in mild cognitive impairment: a systematic review and meta-analysis.Age and ageing · 2025Pooled it
- Machine Learning Approaches for Dementia Detection Through Speech and Gait Analysis: A Systematic Literature Review.Journal of Alzheimer's disease : JAD · 2024Pooled it
- Ten Years of Research on Automatic Voice and Speech Analysis of People With Alzheimer's Disease and Mild Cognitive Impairment: A Systematic Review Article.Frontiers in psychology · 2021Pooled it
- Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer's Disease: A Systematic Review.Journal of Alzheimer's disease : JAD · 2020Pooled it
- AI-Based Measurement Tools for Intrinsic Capacity in Older Adults: Scoping Review.Journal of medical Internet research · 2026Article
- Semantic memory navigation in mild cognitive impairment: Automated markers with neural and biofluid correlates.NeuroImage · 2026Article
- The landscape of dementia research, diagnosis, treatment, and care in Latin America.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Review
- Speech-based digital cognitive assessments for detection of mild cognitive impairment: Validation against paper-based neurocognitive assessment scores.Journal of Alzheimer's disease : JAD · 2025Article
- Optimizing mobile cognitive assessment reduces administration time while maintaining screening accuracy in older adults.Scientific reports · 2025Article
- Artificial intelligence-driven natural language processing for identifying linguistic patterns in Alzheimer's disease and mild cognitive impairment: A study of lexical, syntactic, and cohesive features of speech through picture description tasks.Journal of Alzheimer's disease : JAD · 2025Article
- Validation of reading as a predictor of mild cognitive impairment.Scientific reports · 2025Article
- The use of low-density EEG for the classification of PPA and MCI.Frontiers in human neuroscience · 2025Article
- Discriminant Power of Smartphone-Derived Keystroke Dynamics for Mild Cognitive Impairment Compared to a Neuropsychological Screening Test: Cross-Sectional Study.Journal of medical Internet research · 2024Article
- Storyteller in ADNI4: Application of an early Alzheimer's disease screening tool using brief, remote, and speech-based testing.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- Speech patterns in responses to questions asked by an intelligent virtual agent can help to distinguish between people with early stage neurodegenerative disorders and healthy controls.Clinical linguistics & phonetics · 2024Article
- High frequency post-pause word choices and task-dependent speech behavior characterize connected speech in individuals with mild cognitive impairment.medRxiv : the preprint server for health sciences · 2024Article
- Automatic Spontaneous Speech Analysis for the Detection of Cognitive Functional Decline in Older Adults: Multilanguage Cross-Sectional Study.JMIR aging · 2024Article
- Toolkit to Examine Lifelike Language (TELL): An app to capture speech and language markers of neurodegeneration.Behavior research methods · 2024Article
- Language Markers of Dementia and Their Role in Early Diagnosis of Alzheimer's Disease: Exploring Grammatical and Syntactic Competence via Sentence Repetition.Journal of Alzheimer's disease reports · 2024Article
- Artificial Intelligence in Eye Movements Analysis for Alzheimer's Disease Early Diagnosis.Current Alzheimer research · 2024Review
Corrections and comments
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5 authors.
Funding
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Abstract
Recent work has indicated the potential utility of automated language analysis for the detection of mild cognitive impairment (MCI). Most studies combining language processing and machine learning for the prediction of MCI focus on a single language task; here, we consider a cascaded approach to combine data from multiple language tasks. A cohort of 26 MCI participants and 29 healthy controls completed three language tasks: picture description, reading silently, and reading aloud. Information from each task is captured through different modes (audio, text, eye-tracking, and comprehension questions). Features are extracted from each mode, and used to train a series of cascaded classifiers which output predictions at the level of features, modes, tasks, and finally at the overall session level. The best classification result is achieved through combining the data at the task level (AUC = 0.88, accuracy = 0.83). This outperforms a classifier trained on neuropsychological test scores (AUC = 0.75, accuracy = 0.65) as well as the "early fusion" approach to multimodal classification (AUC = 0.79, accuracy = 0.70). By combining the predictions from the multimodal language classifier and the neuropsychological classifier, this result can be further improved to AUC = 0.90 and accuracy = 0.84. In a correlation analysis, language classifier predictions are found to be moderately correlated (ρ = 0.42) with participant scores on the Rey Auditory Verbal Learning Test (RAVLT). The cascaded approach for multimodal classification improves both system performance and interpretability. This modular architecture can be easily generalized to incorporate different types of classifiers as well as other heterogeneous sources of data (imaging, metabolic, etc.).
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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.