Age density patterns in patients medical conditions: A clustering approach

Autores
Alhasoun, Fahad; Aleissa, Faisal; Alhazzani, May; Moyano, Luis Gregorio; Pinhanez, Claudio; Gonzalez, Marta C.
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This paper presents a data analysis framework to uncover relationships between health conditions, age and sex for a large population of patients. We study a massive heterogeneous sample of 1.7 million patients in Brazil, containing 47 million of health records with detailed medical conditions for visits to medical facilities for a period of 17 months. The findings suggest that medical conditions can be grouped into clusters that share very distinctive densities in the ages of the patients. For each cluster, we further present the ICD-10 chapters within it. Finally, we relate the findings to comorbidity networks, uncovering the relation of the discovered clusters of age densities to comorbidity networks literature.
Fil: Alhasoun, Fahad. Massachusetts Institute of Technology; Estados Unidos
Fil: Aleissa, Faisal. No especifíca;
Fil: Alhazzani, May. Massachusetts Institute of Technology; Estados Unidos
Fil: Moyano, Luis Gregorio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina
Fil: Pinhanez, Claudio. No especifíca;
Fil: Gonzalez, Marta C.. Massachusetts Institute of Technology; Estados Unidos. University of California at Berkeley; Estados Unidos
Materia
DATA ANALYSIS
HEALTHCARE
COMORBIDITY
COMPLEX NETWORKS
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/136072
Acceder
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network_acronym_str CONICETDig
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network_name_str CONICET Digital (CONICET)
spelling Age density patterns in patients medical conditions: A clustering approachAlhasoun, FahadAleissa, FaisalAlhazzani, MayMoyano, Luis GregorioPinhanez, ClaudioGonzalez, Marta C.DATA ANALYSISHEALTHCARECOMORBIDITYCOMPLEX NETWORKShttps://purl.org/becyt/ford/1.7https://purl.org/becyt/ford/1This paper presents a data analysis framework to uncover relationships between health conditions, age and sex for a large population of patients. We study a massive heterogeneous sample of 1.7 million patients in Brazil, containing 47 million of health records with detailed medical conditions for visits to medical facilities for a period of 17 months. The findings suggest that medical conditions can be grouped into clusters that share very distinctive densities in the ages of the patients. For each cluster, we further present the ICD-10 chapters within it. Finally, we relate the findings to comorbidity networks, uncovering the relation of the discovered clusters of age densities to comorbidity networks literature.Fil: Alhasoun, Fahad. Massachusetts Institute of Technology; Estados UnidosFil: Aleissa, Faisal. No especifíca;Fil: Alhazzani, May. Massachusetts Institute of Technology; Estados UnidosFil: Moyano, Luis Gregorio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; ArgentinaFil: Pinhanez, Claudio. No especifíca;Fil: Gonzalez, Marta C.. Massachusetts Institute of Technology; Estados Unidos. University of California at Berkeley; Estados UnidosPublic Library of Science2018-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/136072Alhasoun, Fahad; Aleissa, Faisal; Alhazzani, May; Moyano, Luis Gregorio; Pinhanez, Claudio; et al.; Age density patterns in patients medical conditions: A clustering approach; Public Library of Science; Plos Computational Biology; 14; 6; 7-2018; 1-131553-734XCONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006115info:eu-repo/semantics/altIdentifier/doi/10.1371/journal.pcbi.1006115info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T11:07:08Zoai:ri.conicet.gov.ar:11336/136072instacron: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 11:07:08.553CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Age density patterns in patients medical conditions: A clustering approach
title Age density patterns in patients medical conditions: A clustering approach
spellingShingle Age density patterns in patients medical conditions: A clustering approach
Alhasoun, Fahad
DATA ANALYSIS
HEALTHCARE
COMORBIDITY
COMPLEX NETWORKS
title_short Age density patterns in patients medical conditions: A clustering approach
title_full Age density patterns in patients medical conditions: A clustering approach
title_fullStr Age density patterns in patients medical conditions: A clustering approach
title_full_unstemmed Age density patterns in patients medical conditions: A clustering approach
title_sort Age density patterns in patients medical conditions: A clustering approach
dc.creator.none.fl_str_mv Alhasoun, Fahad
Aleissa, Faisal
Alhazzani, May
Moyano, Luis Gregorio
Pinhanez, Claudio
Gonzalez, Marta C.
author Alhasoun, Fahad
author_facet Alhasoun, Fahad
Aleissa, Faisal
Alhazzani, May
Moyano, Luis Gregorio
Pinhanez, Claudio
Gonzalez, Marta C.
author_role author
author2 Aleissa, Faisal
Alhazzani, May
Moyano, Luis Gregorio
Pinhanez, Claudio
Gonzalez, Marta C.
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv DATA ANALYSIS
HEALTHCARE
COMORBIDITY
COMPLEX NETWORKS
topic DATA ANALYSIS
HEALTHCARE
COMORBIDITY
COMPLEX NETWORKS
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.7
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv This paper presents a data analysis framework to uncover relationships between health conditions, age and sex for a large population of patients. We study a massive heterogeneous sample of 1.7 million patients in Brazil, containing 47 million of health records with detailed medical conditions for visits to medical facilities for a period of 17 months. The findings suggest that medical conditions can be grouped into clusters that share very distinctive densities in the ages of the patients. For each cluster, we further present the ICD-10 chapters within it. Finally, we relate the findings to comorbidity networks, uncovering the relation of the discovered clusters of age densities to comorbidity networks literature.
Fil: Alhasoun, Fahad. Massachusetts Institute of Technology; Estados Unidos
Fil: Aleissa, Faisal. No especifíca;
Fil: Alhazzani, May. Massachusetts Institute of Technology; Estados Unidos
Fil: Moyano, Luis Gregorio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina
Fil: Pinhanez, Claudio. No especifíca;
Fil: Gonzalez, Marta C.. Massachusetts Institute of Technology; Estados Unidos. University of California at Berkeley; Estados Unidos
description This paper presents a data analysis framework to uncover relationships between health conditions, age and sex for a large population of patients. We study a massive heterogeneous sample of 1.7 million patients in Brazil, containing 47 million of health records with detailed medical conditions for visits to medical facilities for a period of 17 months. The findings suggest that medical conditions can be grouped into clusters that share very distinctive densities in the ages of the patients. For each cluster, we further present the ICD-10 chapters within it. Finally, we relate the findings to comorbidity networks, uncovering the relation of the discovered clusters of age densities to comorbidity networks literature.
publishDate 2018
dc.date.none.fl_str_mv 2018-07
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/11336/136072
Alhasoun, Fahad; Aleissa, Faisal; Alhazzani, May; Moyano, Luis Gregorio; Pinhanez, Claudio; et al.; Age density patterns in patients medical conditions: A clustering approach; Public Library of Science; Plos Computational Biology; 14; 6; 7-2018; 1-13
1553-734X
CONICET Digital
CONICET
url http://hdl.handle.net/11336/136072
identifier_str_mv Alhasoun, Fahad; Aleissa, Faisal; Alhazzani, May; Moyano, Luis Gregorio; Pinhanez, Claudio; et al.; Age density patterns in patients medical conditions: A clustering approach; Public Library of Science; Plos Computational Biology; 14; 6; 7-2018; 1-13
1553-734X
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://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006115
info:eu-repo/semantics/altIdentifier/doi/10.1371/journal.pcbi.1006115
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Public Library of Science
publisher.none.fl_str_mv Public Library of Science
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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score 12.982451

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