Automated text-level semantic markers of Alzheimer's disease
| dc.coverage | DOI: 10.1002/dad2.12276 | |
| dc.creator | Sanz, Camila | |
| dc.creator | Carrillo, Facundo | |
| dc.creator | Slachevsky, Andrea | |
| dc.creator | Forno, Gonzalo | |
| dc.creator | Gorno Tempini, Maria Luisa | |
| dc.creator | Villagra, Roque | |
| dc.creator | Ibáñez, Agustín | |
| dc.creator | Tagliazucchi, Enzo | |
| dc.creator | García, Adolfo M. | |
| dc.date | 2022 | |
| dc.date.accessioned | 2026-01-05T21:16:54Z | |
| dc.date.available | 2026-01-05T21:16:54Z | |
| dc.description | <p>Introduction: Automated speech analysis has emerged as a scalable, cost-effective tool to identify persons with Alzheimer's disease dementia (ADD). 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 ADD patients from healthy controls (HCs) based on automated measures of domains typically affected in ADD: 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 discriminated between ADD patients and HC, while yielding near-chance classification between PD patients and HCs. Discussion: Automated discourse-level semantic analyses can reveal objective, interpretable, and specific markers of ADD, bridging well-established neuropsychological targets with digital assessment tools.</p> | eng |
| dc.identifier | https://investigadores.uandes.cl/en/publications/6e4d6699-a55e-49c4-9d84-3bd37eb135dc | |
| dc.identifier.uri | https://repositorio.uandes.cl/handle/uandes/67298 | |
| dc.language | eng | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.source | vol.14 (2022) nr.1 | |
| dc.subject | Alzheimer's disease dementia | |
| dc.subject | automated speech analysis | |
| dc.subject | Parkinson's disease | |
| dc.subject | semantic granularity | |
| dc.subject | semantic variability | |
| dc.subject | SDG 3 - Good Health and Well-being | |
| dc.title | Automated text-level semantic markers of Alzheimer's disease | eng |
| dc.type | Article | eng |
| dc.type | Artículo | spa |