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Comprehensive analysis of clinical data for COVID-19 outcome estimation with machine learning models
dc.contributor.author | Iglesias Morís, Daniel | |
dc.contributor.author | Moura, Joaquim de | |
dc.contributor.author | Marcos, Pedro J. | |
dc.contributor.author | Rey, Enrique | |
dc.contributor.author | Novo Buján, Jorge | |
dc.contributor.author | Ortega Hortas, Marcos | |
dc.date.accessioned | 2023-05-09T18:35:01Z | |
dc.date.available | 2023-05-09T18:35:01Z | |
dc.date.issued | 2023-07 | |
dc.identifier.citation | D. I. Morís, J. de Moura, P. J. Marcos, E. M. Rey, J. Novo, y M. Ortega, «Comprehensive analysis of clinical data for COVID-19 outcome estimation with machine learning models», Biomedical Signal Processing and Control, vol. 84, p. 104818, jul. 2023, doi: 10.1016/j.bspc.2023.104818. | es_ES |
dc.identifier.issn | 1746-8094 | |
dc.identifier.uri | http://hdl.handle.net/2183/33037 | |
dc.description | Funding for open access charge: Universidade da Coruña/CISUG. | es_ES |
dc.description.abstract | [Abstract]: COVID-19 is a global threat for the healthcare systems due to the rapid spread of the pathogen that causes it. In such situation, the clinicians must take important decisions, in an environment where medical resources can be insufficient. In this task, the computer-aided diagnosis systems can be very useful not only in the task of supporting the clinical decisions but also to perform relevant analyses, allowing them to understand better the disease and the factors that can identify the high risk patients. For those purposes, in this work, we use several machine learning algorithms to estimate the outcome of COVID-19 patients given their clinical information. Particularly, we perform 2 different studies: the first one estimates whether the patient is at low or at high risk of death whereas the second estimates if the patient needs hospitalization or not. The results of the analyses of this work show the most relevant features for each studied scenario, as well as the classification performance of the considered machine learning models. In particular, the XGBoost algorithm is able to estimate the need for hospitalization of a patient with an AUC-ROC of 0.8415± 0.0217 while it can also estimate the risk of death with an AUC-ROC of 0.7992±0.0104. Results have demonstrated the great potential of the proposal to determine those patients that need a greater amount of medical resources for being at a higher risk. This provides the healthcare services with a tool to better manage their resources. | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED481A 2021/196 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C 2020/24 | es_ES |
dc.description.sponsorship | Xunta de Galicia; IN845D 2020/38 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G 2019/01 | es_ES |
dc.description.sponsorship | This research was funded by ISCIII, Government of Spain, DTS18/00136 research project; Ministerio de Ciencia e Innovación y Universidades, Government of Spain, RTI2018-095894-B-I00 research project; Ministerio de Ciencia e Innovación, Government of Spain through the research project with reference PID2019-108435RB-I00; CCEU, Xunta de Galicia through the predoctoral grant contract ref. ED481A 2021/196; and Grupos de Referencia Competitiva, grant ref. ED431C 2020/24; Axencia Galega de Innovación (GAIN), Xunta de Galicia, grant ref. IN845D 2020/38; CITIC, Centro de Investigación de Galicia ref. ED431G 2019/01, receives financial support from CCEU, Xunta de Galicia , through the ERDF (80%) and Secretaría Xeral de Universidades (20%). Funding for open access charge: Universidade da Coruña/CISUG. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation | info:eu-repo/grantAgreement/MICINN/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/DTS18%2F00136/ES/PLATAFORMA ONLINE PARA PREVENCIÓN Y DETECCIÓN PRECOZ DE ENFERMEDAD VASCULAR MEDIANTE ANÁLISIS AUTOMATIZADO DE INFORMACIÓN E IMAGEN CLÍNICA | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-095894-B-I00/ES/DESARROLLO DE TECNOLOGIAS INTELIGENTES PARA DIAGNOSTICO DE LA DMAE BASADAS EN EL ANALISIS AUTOMATICO DE NUEVAS MODALIDADES HETEROGENEAS DE ADQUISICION DE IMAGEN OFTALMOLOGICA | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-108435RB-I00/ES/CUANTIFICACION Y CARACTERIZACION COMPUTACIONAL DE IMAGEN MULTIMODAL OFTALMOLOGICA: ESTUDIOS EN ESCLEROSIS MULTIPLE | es_ES |
dc.relation.uri | https://doi.org/10.1016/j.bspc.2023.104818 | es_ES |
dc.rights | Atribución-NoComercial-SinDerivadas 3.0 España | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | * |
dc.subject | COVID-19 | es_ES |
dc.subject | Machine learning | es_ES |
dc.subject | Clinical data | es_ES |
dc.subject | Feature selection | es_ES |
dc.subject | Classification | es_ES |
dc.title | Comprehensive analysis of clinical data for COVID-19 outcome estimation with machine learning models | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Biomedical Signal Processing and Control | es_ES |
UDC.volume | 84 | es_ES |
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