Detecting Parkinson’s disease and its cognitive phenotypes via automated semantic analyses of action stories
- Autores
- García, Adolfo Martín; Escobar Grisales, Daniel; Vásquez Correa, Juan Camilo; Bocanegra, Yamile; Moreno, Leonardo; Carmona, Jairo; Orozco Arroyave, Juan Rafael
- Año de publicación
- 2022
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- 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.
Fil: García, Adolfo Martín. Universidad de San Andrés; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cuyo. Facultad de Educación Elemental y Especial; Argentina
Fil: Escobar Grisales, Daniel. Universidad de Antioquia; Colombia
Fil: Vásquez Correa, Juan Camilo. Universidad de Antioquia; Colombia
Fil: Bocanegra, Yamile. Universidad de Antioquia; Colombia
Fil: Moreno, Leonardo. Hospital Pablo Tobón Uribe; Colombia
Fil: Carmona, Jairo. Universidad de Antioquia; Colombia
Fil: Orozco Arroyave, Juan Rafael. Universidad de Antioquia; Colombia - Materia
-
PARKINSON'S DISEASE
ACTION SEMANTICS
NATURAL LANGUAGE PROCESSING
COGNITIVE PHENOTYPES - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by/2.5/ar/
- Repositorio
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/206010
Ver los metadatos del registro completo
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Detecting Parkinson’s disease and its cognitive phenotypes via automated semantic analyses of action storiesGarcía, Adolfo MartínEscobar Grisales, DanielVásquez Correa, Juan CamiloBocanegra, YamileMoreno, LeonardoCarmona, JairoOrozco Arroyave, Juan RafaelPARKINSON'S DISEASEACTION SEMANTICSNATURAL LANGUAGE PROCESSINGCOGNITIVE PHENOTYPEShttps://purl.org/becyt/ford/6.2https://purl.org/becyt/ford/6https://purl.org/becyt/ford/5.1https://purl.org/becyt/ford/5Action-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.Fil: García, Adolfo Martín. Universidad de San Andrés; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cuyo. Facultad de Educación Elemental y Especial; ArgentinaFil: Escobar Grisales, Daniel. Universidad de Antioquia; ColombiaFil: Vásquez Correa, Juan Camilo. Universidad de Antioquia; ColombiaFil: Bocanegra, Yamile. Universidad de Antioquia; ColombiaFil: Moreno, Leonardo. Hospital Pablo Tobón Uribe; ColombiaFil: Carmona, Jairo. Universidad de Antioquia; ColombiaFil: Orozco Arroyave, Juan Rafael. Universidad de Antioquia; ColombiaNature Research2022-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/206010García, Adolfo Martín; Escobar Grisales, Daniel; Vásquez Correa, Juan Camilo; Bocanegra, Yamile; Moreno, Leonardo; et al.; Detecting Parkinson’s disease and its cognitive phenotypes via automated semantic analyses of action stories; Nature Research; npj Parkinson's Disease; 8; 163; 10-2022; 1-102373-8057CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.nature.com/articles/s41531-022-00422-8info:eu-repo/semantics/altIdentifier/doi/10.1038/s41531-022-00422-8info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T12:05:09Zoai:ri.conicet.gov.ar:11336/206010instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-22 12:05:10.204CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
Detecting Parkinson’s disease and its cognitive phenotypes via automated semantic analyses of action stories |
title |
Detecting Parkinson’s disease and its cognitive phenotypes via automated semantic analyses of action stories |
spellingShingle |
Detecting Parkinson’s disease and its cognitive phenotypes via automated semantic analyses of action stories García, Adolfo Martín PARKINSON'S DISEASE ACTION SEMANTICS NATURAL LANGUAGE PROCESSING COGNITIVE PHENOTYPES |
title_short |
Detecting Parkinson’s disease and its cognitive phenotypes via automated semantic analyses of action stories |
title_full |
Detecting Parkinson’s disease and its cognitive phenotypes via automated semantic analyses of action stories |
title_fullStr |
Detecting Parkinson’s disease and its cognitive phenotypes via automated semantic analyses of action stories |
title_full_unstemmed |
Detecting Parkinson’s disease and its cognitive phenotypes via automated semantic analyses of action stories |
title_sort |
Detecting Parkinson’s disease and its cognitive phenotypes via automated semantic analyses of action stories |
dc.creator.none.fl_str_mv |
García, Adolfo Martín Escobar Grisales, Daniel Vásquez Correa, Juan Camilo Bocanegra, Yamile Moreno, Leonardo Carmona, Jairo Orozco Arroyave, Juan Rafael |
author |
García, Adolfo Martín |
author_facet |
García, Adolfo Martín Escobar Grisales, Daniel Vásquez Correa, Juan Camilo Bocanegra, Yamile Moreno, Leonardo Carmona, Jairo Orozco Arroyave, Juan Rafael |
author_role |
author |
author2 |
Escobar Grisales, Daniel Vásquez Correa, Juan Camilo Bocanegra, Yamile Moreno, Leonardo Carmona, Jairo Orozco Arroyave, Juan Rafael |
author2_role |
author author author author author author |
dc.subject.none.fl_str_mv |
PARKINSON'S DISEASE ACTION SEMANTICS NATURAL LANGUAGE PROCESSING COGNITIVE PHENOTYPES |
topic |
PARKINSON'S DISEASE ACTION SEMANTICS NATURAL LANGUAGE PROCESSING COGNITIVE PHENOTYPES |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/6.2 https://purl.org/becyt/ford/6 https://purl.org/becyt/ford/5.1 https://purl.org/becyt/ford/5 |
dc.description.none.fl_txt_mv |
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. Fil: García, Adolfo Martín. Universidad de San Andrés; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cuyo. Facultad de Educación Elemental y Especial; Argentina Fil: Escobar Grisales, Daniel. Universidad de Antioquia; Colombia Fil: Vásquez Correa, Juan Camilo. Universidad de Antioquia; Colombia Fil: Bocanegra, Yamile. Universidad de Antioquia; Colombia Fil: Moreno, Leonardo. Hospital Pablo Tobón Uribe; Colombia Fil: Carmona, Jairo. Universidad de Antioquia; Colombia Fil: Orozco Arroyave, Juan Rafael. Universidad de Antioquia; Colombia |
description |
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. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-10 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
format |
article |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://hdl.handle.net/11336/206010 García, Adolfo Martín; Escobar Grisales, Daniel; Vásquez Correa, Juan Camilo; Bocanegra, Yamile; Moreno, Leonardo; et al.; Detecting Parkinson’s disease and its cognitive phenotypes via automated semantic analyses of action stories; Nature Research; npj Parkinson's Disease; 8; 163; 10-2022; 1-10 2373-8057 CONICET Digital CONICET |
url |
http://hdl.handle.net/11336/206010 |
identifier_str_mv |
García, Adolfo Martín; Escobar Grisales, Daniel; Vásquez Correa, Juan Camilo; Bocanegra, Yamile; Moreno, Leonardo; et al.; Detecting Parkinson’s disease and its cognitive phenotypes via automated semantic analyses of action stories; Nature Research; npj Parkinson's Disease; 8; 163; 10-2022; 1-10 2373-8057 CONICET Digital CONICET |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/url/https://www.nature.com/articles/s41531-022-00422-8 info:eu-repo/semantics/altIdentifier/doi/10.1038/s41531-022-00422-8 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/2.5/ar/ |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by/2.5/ar/ |
dc.format.none.fl_str_mv |
application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
Nature Research |
publisher.none.fl_str_mv |
Nature Research |
dc.source.none.fl_str_mv |
reponame:CONICET Digital (CONICET) instname:Consejo Nacional de Investigaciones Científicas y Técnicas |
reponame_str |
CONICET Digital (CONICET) |
collection |
CONICET Digital (CONICET) |
instname_str |
Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.name.fl_str_mv |
CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.mail.fl_str_mv |
dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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1846782404043210752 |
score |
12.982451 |