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dc.contributor.authorBoukichou Abdelkader, Nisa
dc.contributor.authorMontero Alonso, Miguel Ángel 
dc.date.accessioned2022-04-05T12:04:17Z
dc.date.available2022-04-05T12:04:17Z
dc.date.issued2022-02-23
dc.identifier.citationBoukichou-Abdelkader, N.; Montero-Alonso,M.Á.;Muñoz-García, A. Different Routes or Methods of Application for Dimensionality Reduction in Multicenter Studies Databases. Mathematics 2022, 10, 696. [https://doi.org/10.3390/math10050696]es_ES
dc.identifier.urihttp://hdl.handle.net/10481/74147
dc.description.abstractTechnological progress and digital transformation, which began with Big Data and Artificial Intelligence (AI), are currently transforming ways of working in all fields, to support decision-making, particularly in multicenter research. This study analyzed a sample of 5178 hospital patients, suffering from exacerbation of chronic obstructive pulmonary disease (eCOPD). Because of differences in disease stages and progression, the clinical pathologies and characteristics of the patients were extremely diverse. Our objective was thus to reduce dimensionality by projecting the data onto a lower dimensional subspace. The results obtained show that principal component analysis (PCA) is the most effective linear technique for dimensionality reduction. Four patient profile groups are generated with similar affinity and characteristics. In conclusion, dimensionality reduction is found to be an effective technique that permits the visualization of early indications of clinical patterns with similar characteristics. This is valuable since the development of other pathologies (chronic diseases) over any given time period influences clinical parameters. If healthcare professionals can have access to such information beforehand, this can significantly improve the quality of patient care, since this type of study is based on a multitude of data-variables that can be used to evaluate and monitor the clinical status of the patient.es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.rightsAtribución 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectPCAes_ES
dc.subjectMICE es_ES
dc.subjectRF&IVes_ES
dc.subjectSimulationes_ES
dc.subjecteCOPDes_ES
dc.titleDifferent Routes or Methods of Application for Dimensionality Reduction in Multicenter Studies Databaseses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.3390/math10050696
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES


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Atribución 3.0 España
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