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Random Forest-Based Prediction of Stroke Outcome
dc.contributor.author | Fernández-Lozano, Carlos | |
dc.contributor.author | Hervella, Pablo | |
dc.contributor.author | Mato-Abad, Virginia | |
dc.contributor.author | Rodríguez-Yáñez, Manuel | |
dc.contributor.author | Suárez-Garaboa, Sonia | |
dc.contributor.author | López Dequidt, Iria Alejandra | |
dc.contributor.author | Estany-Gestal, Ana | |
dc.contributor.author | Sobrino, Tomás | |
dc.contributor.author | Campos, Francisco | |
dc.contributor.author | Castillo, José | |
dc.contributor.author | Rodríguez-Yáñez, Santiago | |
dc.contributor.author | Iglesias Rey, Ramón | |
dc.date.accessioned | 2021-07-02T18:21:26Z | |
dc.date.available | 2021-07-02T18:21:26Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | Fernandez-Lozano, C., Hervella, P., Mato-Abad, V. et al. Random forest-based prediction of stroke outcome. Sci Rep 11, 10071 (2021). https://doi.org/10.1038/s41598-021-89434-7 | es_ES |
dc.identifier.issn | 2045-2322 | |
dc.identifier.uri | http://hdl.handle.net/2183/28159 | |
dc.description.abstract | [Abstract] We research into the clinical, biochemical and neuroimaging factors associated with the outcome of stroke patients to generate a predictive model using machine learning techniques for prediction of mortality and morbidity 3-months after admission. The dataset consisted of patients with ischemic stroke (IS) and non-traumatic intracerebral hemorrhage (ICH) admitted to Stroke Unit of a European Tertiary Hospital prospectively registered. We identified the main variables for machine learning Random Forest (RF), generating a predictive model that can estimate patient mortality/morbidity according to the following groups: (1) IS + ICH, (2) IS, and (3) ICH. A total of 6022 patients were included: 4922 (mean age 71.9 ± 13.8 years) with IS and 1100 (mean age 73.3 ± 13.1 years) with ICH. NIHSS at 24, 48 h and axillary temperature at admission were the most important variables to consider for evolution of patients at 3-months. IS + ICH group was the most stable for mortality prediction [0.904 ± 0.025 of area under the receiver operating characteristics curve (AUC)]. IS group presented similar results, although variability between experiments was slightly higher (0.909 ± 0.032 of AUC). ICH group was the one in which RF had more problems to make adequate predictions (0.9837 vs. 0.7104 of AUC). There were no major differences between IS and IS + ICH groups according to morbidity prediction (0.738 and 0.755 of AUC) but, after checking normality with a Shapiro Wilk test with the null hypothesis that the data follow a normal distribution, it was rejected with W = 0.93546 (p-value < 2.2e−16). Conditions required for a parametric test do not hold, and we performed a paired Wilcoxon Test assuming the null hypothesis that all the groups have the same performance. The null hypothesis was rejected with a value < 2.2e−16, so there are statistical differences between IS and ICH groups. In conclusion, machine learning algorithms RF can be effectively used in stroke patients for long-term outcome prediction of mortality and morbidity. | es_ES |
dc.description.sponsorship | This study was partially supported by grants from the Spanish Ministry of Science and Innovation (SAF2017-84267-R), Xunta de Galicia (Axencia Galega de Innovación (GAIN): IN607A2018/3), Instituto de Salud Carlos III (ISCIII) (PI17/00540, PI17/01103), Spanish Research Network on Cerebrovascular Diseases RETICS-INVICTUS PLUS (RD16/0019) and by the European Union FEDER program. T. Sobrino (CPII17/00027), F. Campos (CPII19/00020) are recipients of research contracts from the Miguel Servet Program (Instituto de Salud Carlos III). General Directorate of Culture, Education and University Management of Xunta de Galicia (ED431G/01,252 ED431D 2017/16), “Galician Network for Colorectal Cancer Research" (Ref. ED431D 2017/23), Competitive Reference Groups (ED431C 2018/49), Spanish Ministry of Economy and Competitiveness via funding of the unique installation BIOCAI (UNLC08-1E-002, UNLC13-13–3503), European Regional Development Funds (FEDER). | es_ES |
dc.description.sponsorship | Xunta de Galicia; IN607A2018/3 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G/01,252 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431D 2017/16 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Nature Research | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/SAF2017-84267-R/ES/NANOSONDAS DIAPEUTICAS AVANZADAS PARA IMAGENES MOLECULARES: EVALUACION DE LA DISFUNCION ENDOTELIAL EN LA ISQUEMIA CEREBRAL | |
dc.relation | info:eu-repo/grantAgreement/MINECO//PI17%2F00540/ES/Implantación de la Medicina Personalizada para el estudio y tratamiento de Enfermedades Cerebrovasculares como CADASIL | |
dc.relation | info:eu-repo/grantAgreement/MINECO//PI17%2F01103/ES/Tromboimagen molecular por resonancia magnética con nanopartículas duales vectorizadas | |
dc.relation | info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/RD16%2F0019%2F0004/ES/Red de Enfermedades Vasculares Cerebrales. INVICTUS PLUS | |
dc.relation | info:eu-repo/grantAgreement/MINECO//CPII17%2F00027/ES/ | |
dc.relation | info:eu-repo/grantAgreement/MICINN/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/CPII19%2F00020/ES/ | |
dc.relation | info:eu-repo/grantAgreement/MEC/Plan Nacional de I+D+i 2008-2011/UNLC08-1E-002/ES/Infraestructura computacional para la Red Gallega de Bioinformática | |
dc.relation | info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/UNLC13-1E-3503/ES/ | |
dc.relation.uri | https://doi.org/10.1038/s41598-021-89434-7 | es_ES |
dc.rights | Atribución 4.0 Internacional | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | Medical research | es_ES |
dc.subject | Stroke | es_ES |
dc.title | Random Forest-Based Prediction of Stroke Outcome | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Scientific Reports | es_ES |
UDC.volume | 11 | es_ES |
UDC.issue | 1 | es_ES |
dc.identifier.doi | 10.1038/s41598-021-89434-7 |
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