Abstract
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.
Highlights
In recent years, technological progress and digital transformation, which began with Big Data and Artificial Intelligence (AI), have transformed society
The statistical software R [24] is used to perform the analysis because it is able to compensate aspects of the imputation of missing values [26] in certain variables of the dataset. This is achieved with various methods in the multiple imputation by chained equations (MICE) package [27], with a view to completing and improving the final results
R provides the platform for the application of the different multivariate analysis techniques using principal components analysis (PCA), random forest (RF)&IV, and PA-RES in order to reduce and optimize the dimensional space of the dataset
Summary
Technological progress and digital transformation, which began with Big Data and Artificial Intelligence (AI), have transformed society. The current COVID-19 pandemic has had a devastating effect on the world population at all socioeconomic levels and has significantly impacted healthcare and biomedical research. This has led institutions to explore possible synergies between the fields of computational statistics and healthcare with a view to analyze the large quantity of data in medical records. The information in these databases could be effectively used to support decision-making and improve the quality of patient care [1]. This study highlights the need to explore more effective ways of improving data searches in clinical profiles
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