Different Routes or Methods of Application for Dimensionality Reduction in Multicenter Studies Databases
Metadatos
Afficher la notice complèteEditorial
MDPI
Materia
PCA MICE RF&IV Simulation eCOPD
Date
2022-02-23Referencia bibliográfica
Boukichou-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]
Résumé
Technological 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.