In:
PLOS ONE, Public Library of Science (PLoS), Vol. 18, No. 4 ( 2023-4-13), p. e0284150-
Abstract:
With the COVID-19 pandemic having caused unprecedented numbers of infections and deaths, large research efforts have been undertaken to increase our understanding of the disease and the factors which determine diverse clinical evolutions. Here we focused on a fully data-driven exploration regarding which factors (clinical or otherwise) were most informative for SARS-CoV-2 pneumonia severity prediction via machine learning (ML). In particular, feature selection techniques (FS), designed to reduce the dimensionality of data, allowed us to characterize which of our variables were the most useful for ML prognosis. We conducted a multi-centre clinical study, enrolling n = 1548 patients hospitalized due to SARS-CoV-2 pneumonia: where 792, 238, and 598 patients experienced low, medium and high-severity evolutions, respectively. Up to 106 patient-specific clinical variables were collected at admission, although 14 of them had to be discarded for containing ⩾60% missing values. Alongside 7 socioeconomic attributes and 32 exposures to air pollution (chronic and acute), these became d = 148 features after variable encoding. We addressed this ordinal classification problem both as a ML classification and regression task. Two imputation techniques for missing data were explored, along with a total of 166 unique FS algorithm configurations: 46 filters, 100 wrappers and 20 embeddeds. Of these, 21 setups achieved satisfactory bootstrap stability (⩾0.70) with reasonable computation times: 16 filters, 2 wrappers, and 3 embeddeds. The subsets of features selected by each technique showed modest Jaccard similarities across them. However, they consistently pointed out the importance of certain explanatory variables. Namely: patient’s C-reactive protein (CRP), pneumonia severity index (PSI), respiratory rate (RR) and oxygen levels –saturation Sp O2, quotients Sp O2/RR and arterial Sat O2/Fi O2–, the neutrophil-to-lymphocyte ratio (NLR) –to certain extent, also neutrophil and lymphocyte counts separately–, lactate dehydrogenase (LDH), and procalcitonin (PCT) levels in blood. A remarkable agreement has been found a posteriori between our strategy and independent clinical research works investigating risk factors for COVID-19 severity. Hence, these findings stress the suitability of this type of fully data-driven approaches for knowledge extraction, as a complementary to clinical perspectives.
Type of Medium:
Online Resource
ISSN:
1932-6203
DOI:
10.1371/journal.pone.0284150
DOI:
10.1371/journal.pone.0284150.g001
DOI:
10.1371/journal.pone.0284150.g002
DOI:
10.1371/journal.pone.0284150.g003
DOI:
10.1371/journal.pone.0284150.g004
DOI:
10.1371/journal.pone.0284150.g005
DOI:
10.1371/journal.pone.0284150.t001
DOI:
10.1371/journal.pone.0284150.t002
DOI:
10.1371/journal.pone.0284150.t003
DOI:
10.1371/journal.pone.0284150.t004
DOI:
10.1371/journal.pone.0284150.t005
DOI:
10.1371/journal.pone.0284150.t006
DOI:
10.1371/journal.pone.0284150.s001
DOI:
10.1371/journal.pone.0284150.s002
DOI:
10.1371/journal.pone.0284150.r001
DOI:
10.1371/journal.pone.0284150.r002
DOI:
10.1371/journal.pone.0284150.r003
DOI:
10.1371/journal.pone.0284150.r004
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2023
detail.hit.zdb_id:
2267670-3
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