Artículo
Automated text-level semantic markers of Alzheimer's disease
Sanz, Camila
; Carrillo, Facundo
; Slachevsky, Andrea; Forno, Gonzalo; Gorno Tempini, María L.; Villagra, Roque; Ibañez, Agustin Mariano
; Tagliazucchi, Enzo Rodolfo
; García, Adolfo Martín
Fecha de publicación:
01/2022
Editorial:
Wiley
Revista:
Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring
ISSN:
2352-8729
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
INTRODUCTION: Automated speech analysis has emerged as a scalable, cost-effective tool to identify persons with Alzheimer?s disease (AD). Yet, most research is undermined by low interpretability and specificity. METHODS: Combining statistical and machine learning analyses of natural speech data, we aimed to discriminate AD dementia (ADD) patients from healthy controls (HCs) based on automated measures of domains typically affected in AD: semantic granularity (coarseness of concepts) and ongoing semantic variability (conceptual closeness of successive words). To test for specificity, we replicated the analyses on Parkinson?s disease (PD) patients. RESULTS: Relative to controls, ADD (but not PD) patients exhibited significant differences in both measures. Also, these features robustly classified between ADD patients and HCs (AUC = 0.8), yielding near-chance classification between PD patients and HCs (AUC = 0.65). DISCUSSION: Automated discourse-level semantic analyses can reveal objective, interpretable, and specific markers of ADD, bridging well-established neuropsychological targets with digital assessment tools.
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Articulos(IFIBA)
Articulos de INST.DE FISICA DE BUENOS AIRES
Articulos de INST.DE FISICA DE BUENOS AIRES
Articulos(SEDE CENTRAL)
Articulos de SEDE CENTRAL
Articulos de SEDE CENTRAL
Citación
Sanz, Camila; Carrillo, Facundo; Slachevsky, Andrea; Forno, Gonzalo; Gorno Tempini, María L.; et al.; Automated text-level semantic markers of Alzheimer's disease; Wiley; Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring; 14; 1; 1-2022; 1-10
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