Clinical Characteristics and prognostic factors for Intensive Care Unit Admission of Patients With COVID-19. Retrospective Study Using Machine Learning and Natural Language Processing.

13 p.

Detalles Bibliográficos
Autores: Izquierdo Alonso, José Luis|||0000-0002-1671-2243, Ancochea, Julio, Soriano Ortiz, Juan Bautista
Tipo de recurso: artículo
Fecha de publicación:2020
País:España
Institución:Universidad de Alcalá (UAH)
Repositorio:e_Buah Biblioteca Digital Universidad de Alcalá
Idioma:inglés
OAI Identifier:oai:ebuah.uah.es:10017/59401
Acceso en línea:http://hdl.handle.net/10017/59401
https://dx.doi.org/10.2196/21801
Access Level:acceso abierto
Palabra clave:Artificial intelligence
Big data
COVID-19
Electronic health records
Tachypnea
SARS-CoV-2
Predictive model
Medicina
Medicine
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spelling Clinical Characteristics and prognostic factors for Intensive Care Unit Admission of Patients With COVID-19. Retrospective Study Using Machine Learning and Natural Language Processing.Izquierdo Alonso, José Luis|||0000-0002-1671-2243Ancochea, JulioSoriano Ortiz, Juan BautistaArtificial intelligenceBig dataCOVID-19Electronic health recordsTachypneaSARS-CoV-2Predictive modelMedicinaMedicine13 p.Background: Many factors involved in the onset and clinical course of the ongoing COVID-19 pandemic are still unknown. Although big data analytics and artificial intelligence are widely used in the realms of health and medicine, researchers are only beginning to use these tools to explore the clinical characteristics and predictive factors of patients with COVID-19. Objective: Our primary objectives are to describe the clinical characteristics and determine the factors that predict intensive care unit (ICU) admission of patients with COVID-19. Determining these factors using a well-defined population can increase our understanding of the real-world epidemiology of the disease. Methods: We used a combination of classic epidemiological methods, natural language processing (NLP), and machine learning (for predictive modeling) to analyze the electronic health records (EHRs) of patients with COVID-19. We explored the unstructured free text in the EHRs within the Servicio de Salud de Castilla-La Mancha (SESCAM) Health Care Network (Castilla-La Mancha, Spain) from the entire population with available EHRs (1,364,924 patients) from January 1 to March 29, 2020. We extracted related clinical information regarding diagnosis, progression, and outcome for all COVID-19 cases. Results: A total of 10,504 patients with a clinical or polymerase chain reaction?confirmed diagnosis of COVID-19 were identified; 5519 (52.5%) were male, with a mean age of 58.2 years (SD 19.7). Upon admission, the most common symptoms were cough, fever, and dyspnea; however, all three symptoms occurred in fewer than half of the cases. Overall, 6.1% (83/1353) of hospitalized patients required ICU admission. Using a machine-learning, data-driven algorithm, we identified that a combination of age, fever, and tachypnea was the most parsimonious predictor of ICU admission; patients younger than 56 years, without tachypnea, and temperature 39 ºC without respiratory crackles) were not admitted to the ICU. In contrast, patients with COVID-19 aged 40 to 79 years were likely to be admitted to the ICU if they had tachypnea and delayed their visit to the emergency department after being seen in primary care. Conclusions: Our results show that a combination of easily obtainable clinical variables (age, fever, and tachypnea with or without respiratory crackles) predicts whether patients with COVID-19 will require ICU admission20202020-10-28journal articlehttp://purl.org/coar/resource_type/c_6501NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10017/59401https://dx.doi.org/10.2196/21801reponame:e_Buah Biblioteca Digital Universidad de Alcaláinstname:Universidad de Alcalá (UAH)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:ebuah.uah.es:10017/594012026-06-18T11:13:07Z
dc.title.none.fl_str_mv Clinical Characteristics and prognostic factors for Intensive Care Unit Admission of Patients With COVID-19. Retrospective Study Using Machine Learning and Natural Language Processing.
title Clinical Characteristics and prognostic factors for Intensive Care Unit Admission of Patients With COVID-19. Retrospective Study Using Machine Learning and Natural Language Processing.
spellingShingle Clinical Characteristics and prognostic factors for Intensive Care Unit Admission of Patients With COVID-19. Retrospective Study Using Machine Learning and Natural Language Processing.
Izquierdo Alonso, José Luis|||0000-0002-1671-2243
Artificial intelligence
Big data
COVID-19
Electronic health records
Tachypnea
SARS-CoV-2
Predictive model
Medicina
Medicine
title_short Clinical Characteristics and prognostic factors for Intensive Care Unit Admission of Patients With COVID-19. Retrospective Study Using Machine Learning and Natural Language Processing.
title_full Clinical Characteristics and prognostic factors for Intensive Care Unit Admission of Patients With COVID-19. Retrospective Study Using Machine Learning and Natural Language Processing.
title_fullStr Clinical Characteristics and prognostic factors for Intensive Care Unit Admission of Patients With COVID-19. Retrospective Study Using Machine Learning and Natural Language Processing.
title_full_unstemmed Clinical Characteristics and prognostic factors for Intensive Care Unit Admission of Patients With COVID-19. Retrospective Study Using Machine Learning and Natural Language Processing.
title_sort Clinical Characteristics and prognostic factors for Intensive Care Unit Admission of Patients With COVID-19. Retrospective Study Using Machine Learning and Natural Language Processing.
dc.creator.none.fl_str_mv Izquierdo Alonso, José Luis|||0000-0002-1671-2243
Ancochea, Julio
Soriano Ortiz, Juan Bautista
author Izquierdo Alonso, José Luis|||0000-0002-1671-2243
author_facet Izquierdo Alonso, José Luis|||0000-0002-1671-2243
Ancochea, Julio
Soriano Ortiz, Juan Bautista
author_role author
author2 Ancochea, Julio
Soriano Ortiz, Juan Bautista
author2_role author
author
dc.subject.none.fl_str_mv Artificial intelligence
Big data
COVID-19
Electronic health records
Tachypnea
SARS-CoV-2
Predictive model
Medicina
Medicine
topic Artificial intelligence
Big data
COVID-19
Electronic health records
Tachypnea
SARS-CoV-2
Predictive model
Medicina
Medicine
description 13 p.
publishDate 2020
dc.date.none.fl_str_mv 2020
2020-10-28
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10017/59401
https://dx.doi.org/10.2196/21801
url http://hdl.handle.net/10017/59401
https://dx.doi.org/10.2196/21801
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:e_Buah Biblioteca Digital Universidad de Alcalá
instname:Universidad de Alcalá (UAH)
instname_str Universidad de Alcalá (UAH)
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