| Título : |
Detecting Parkinson’s disease and its cognitive phenotypes via automated semantic analyses of action stories |
| Tipo de documento : |
documento electrónico |
| Autores : |
Leonardo Fabio Moreno Gómez, |
| Fecha de publicación : |
2022 |
| Títulos uniformes : |
NPJ Parkinson's Disease
|
| Idioma : |
Inglés (eng) |
| Resumen : |
Action-concept outcomes are useful targets to identify Parkinson’s disease (PD) patients and differentiate between those with and without mild cognitive impairment (PD-MCI, PD-nMCI). Yet, most approaches employ burdensome examiner-dependent tasks, limiting their utility. We introduce a framework capturing action-concept markers automatically in natural speech. Patients from both subgroups and controls retold an action-laden and a non-action-laden text (AT, nAT). In each retelling, we weighed action and non-action concepts through our automated Proximity-to-Reference-Semantic-Field (P-RSF) metric, for analysis via ANCOVAs (controlling for cognitive dysfunction) and support vector machines. Patients were differentiated from controls based on AT (but not nAT) P-RSF scores. The same occurred in PD-nMCI patients. Conversely, PD-MCI patients exhibited reduced P-RSF scores for both texts. Direct discrimination between patient subgroups was not systematic, but it yielded best outcomes via AT scores. Our approach outperformed classifiers based on corpus-derived embeddings. This framework opens scalable avenues to support PD diagnosis and phenotyping. |
| Mención de responsabilidad : |
Adolfo M. García, Daniel Escobar-Grisales, Juan Camilo Vásquez Correa, Yamile Bocanegra, Leonardo Moreno, Jairo Carmona & Juan Rafael Orozco-Arroyave |
| Referencia : |
NPJ Parkinsons Dis. 2022 Nov 25;8(1):163. |
| DOI (Digital Object Identifier) : |
10.1038/s41531-022-00422-8 |
| PMID : |
36434017 |
| Derechos de uso : |
CC BY |
| En línea : |
https://www.nature.com/articles/s41531-022-00422-8 |
| Enlace permanente : |
https://hospitalpablotobon.cloudbiteca.com/pmb/opac_css/index.php?lvl=notice_display&id=6081 |
Detecting Parkinson’s disease and its cognitive phenotypes via automated semantic analyses of action stories [documento electrónico] / Leonardo Fabio Moreno Gómez, . - 2022. Obra : NPJ Parkinson's DiseaseIdioma : Inglés ( eng) | Resumen : |
Action-concept outcomes are useful targets to identify Parkinson’s disease (PD) patients and differentiate between those with and without mild cognitive impairment (PD-MCI, PD-nMCI). Yet, most approaches employ burdensome examiner-dependent tasks, limiting their utility. We introduce a framework capturing action-concept markers automatically in natural speech. Patients from both subgroups and controls retold an action-laden and a non-action-laden text (AT, nAT). In each retelling, we weighed action and non-action concepts through our automated Proximity-to-Reference-Semantic-Field (P-RSF) metric, for analysis via ANCOVAs (controlling for cognitive dysfunction) and support vector machines. Patients were differentiated from controls based on AT (but not nAT) P-RSF scores. The same occurred in PD-nMCI patients. Conversely, PD-MCI patients exhibited reduced P-RSF scores for both texts. Direct discrimination between patient subgroups was not systematic, but it yielded best outcomes via AT scores. Our approach outperformed classifiers based on corpus-derived embeddings. This framework opens scalable avenues to support PD diagnosis and phenotyping. |
| Mención de responsabilidad : |
Adolfo M. García, Daniel Escobar-Grisales, Juan Camilo Vásquez Correa, Yamile Bocanegra, Leonardo Moreno, Jairo Carmona & Juan Rafael Orozco-Arroyave |
| Referencia : |
NPJ Parkinsons Dis. 2022 Nov 25;8(1):163. |
| DOI (Digital Object Identifier) : |
10.1038/s41531-022-00422-8 |
| PMID : |
36434017 |
| Derechos de uso : |
CC BY |
| En línea : |
https://www.nature.com/articles/s41531-022-00422-8 |
| Enlace permanente : |
https://hospitalpablotobon.cloudbiteca.com/pmb/opac_css/index.php?lvl=notice_display&id=6081 |
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