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
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/206010
Acceder
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network_name_str CONICET Digital (CONICET)
spelling 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
_version_ 1846782404043210752
score 12.982451

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