Academic Journal
Prediction of Alzheimer's disease progression within 6 years using speech: A novel approach leveraging language models.
| Τίτλος: | Prediction of Alzheimer's disease progression within 6 years using speech: A novel approach leveraging language models. |
|---|---|
| Συγγραφείς: | Amini S; Department of Electrical & Computer Engineering, Division of Systems Engineering, and Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA., Hao B; Department of Electrical & Computer Engineering, Division of Systems Engineering, and Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA., Yang J; Department of Electrical & Computer Engineering, Division of Systems Engineering, and Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA., Karjadi C; Framingham Heart Study, Boston University, Framingham, Massachusetts, USA., Kolachalama VB; Department of Medicine, Boston University School of Medicine, Boston, Massachusetts, USA.; Faculty of Computing & Data Sciences, Boston University, Boston, Massachusetts, USA.; Department of Computer Science, Boston University, Boston, Massachusetts, USA., Au R; Framingham Heart Study, Boston University, Framingham, Massachusetts, USA.; Departments of Anatomy & Neurobiology, Neurology, and Epidemiology, Boston University School of Medicine and School of Public Health, Boston, Massachusetts, USA., Paschalidis IC; Department of Electrical & Computer Engineering, Division of Systems Engineering, and Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA.; Faculty of Computing & Data Sciences, Boston University, Boston, Massachusetts, USA. |
| Πηγή: | Alzheimer's & dementia : the journal of the Alzheimer's Association [Alzheimers Dement] 2024 Aug; Vol. 20 (8), pp. 5262-5270. Date of Electronic Publication: 2024 Jun 25. |
| Τύπος έκδοσης: | Journal Article; Research Support, N.I.H., Extramural; Research Support, U.S. Gov't, Non-P.H.S. |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: John Wiley & Sons, Ltd Country of Publication: United States NLM ID: 101231978 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1552-5279 (Electronic) Linking ISSN: 15525260 NLM ISO Abbreviation: Alzheimers Dement Subsets: MEDLINE |
| Imprint Name(s): | Publication: 2020- : Hoboken, NJ : John Wiley & Sons, Ltd. Original Publication: Orlando, FL : Elsevier, Inc. |
| Ιατρικοί όροι (MeSH): | Alzheimer Disease*/diagnosis , Cognitive Dysfunction*/diagnosis , Neuropsychological Tests*/statistics & numerical data , Disease Progression* , Speech* , Machine Learning*, Humans ; Female ; Male ; Aged ; Natural Language Processing ; Aged, 80 and over |
| Περίληψη: | Introduction: Identification of individuals with mild cognitive impairment (MCI) who are at risk of developing Alzheimer's disease (AD) is crucial for early intervention and selection of clinical trials. Methods: We applied natural language processing techniques along with machine learning methods to develop a method for automated prediction of progression to AD within 6 years using speech. The study design was evaluated on the neuropsychological test interviews of n = 166 participants from the Framingham Heart Study, comprising 90 progressive MCI and 76 stable MCI cases. Results: Our best models, which used features generated from speech data, as well as age, sex, and education level, achieved an accuracy of 78.5% and a sensitivity of 81.1% to predict MCI-to-AD progression within 6 years. Discussion: The proposed method offers a fully automated procedure, providing an opportunity to develop an inexpensive, broadly accessible, and easy-to-administer screening tool for MCI-to-AD progression prediction, facilitating development of remote assessment. Highlights: Voice recordings from neuropsychological exams coupled with basic demographics can lead to strong predictive models of progression to dementia from mild cognitive impairment. The study leveraged AI methods for speech recognition and processed the resulting text using language models. The developed AI-powered pipeline can lead to fully automated assessment that could enable remote and cost-effective screening and prognosis for Alzehimer's disease. (© 2024 The Author(s). Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.) |
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| Grant Information: | R01 AG016495 United States AG NIA NIH HHS; R01 GM135930 United States GM NIGMS NIH HHS; RF1 AG062109 United States AG NIA NIH HHS; DMS-1664644 NSF; AG16495 United States AG NIA NIH HHS; AG072654 United States AG NIA NIH HHS; AG068753 United States AG NIA NIH HHS; R01 AG072654 United States AG NIA NIH HHS; AG008122 United States AG NIA NIH HHS; IIS-1914792 NSF; U19 AG068753 United States AG NIA NIH HHS; R01 AG062109 United States AG NIA NIH HHS; CCF-2200052 NSF; UL54 TR00413 United States GF NIH HHS; RF1 AG072654 United States AG NIA NIH HHS; R01 GM135930 United States GF NIH HHS; R01 AG008122 United States AG NIA NIH HHS; N01-HC-25195 United States HL NHLBI NIH HHS; R56 AG062109 United States AG NIA NIH HHS; AG062109 United States AG NIA NIH HHS |
| Contributed Indexing: | Keywords: Alzheimer's disease prognosis; Framingham Heart Study; natural language processing; neuropsychological test |
| Entry Date(s): | Date Created: 20240626 Date Completed: 20240828 Latest Revision: 20260314 |
| Update Code: | 20260314 |
| PubMed Central ID: | PMC11350035 |
| DOI: | 10.1002/alz.13886 |
| PMID: | 38924662 |
| Βάση Δεδομένων: | MEDLINE |
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