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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/136072
Ver los metadatos del registro completo
id |
CONICETDig_e6d6dcb83cf988d6024f78f463e575b6 |
---|---|
oai_identifier_str |
oai:ri.conicet.gov.ar:11336/136072 |
network_acronym_str |
CONICETDig |
repository_id_str |
3498 |
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 Técnicas2025-09-29T09:38:50Zoai: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:34982025-09-29 09:38:50.452CONICET 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 |
_version_ |
1844613228050513920 |
score |
13.070432 |