Utilizing machine learning for survival analysis to identify risk factors for COVID-19 intensive care unit admission: A retrospective cohort study from the United Arab Emirates

{ 'indexed': {'date-parts': [[2024, 1, 12]], 'date-time': '2024-01-12T00:23:24Z', 'timestamp': 1705019004106}, 'reference-count': 44, 'publisher': 'Public Library of Science (PLoS)', 'issue': '1', 'license': [ { 'start': { 'date-parts': [[2024, 1, 11]], 'date-time': '2024-01-11T00:00:00Z', 'timestamp': 1704931200000}, 'content-version': 'vor', 'delay-in-days': 0, 'URL': 'http://creativecommons.org/licenses/by/4.0/'}], 'content-domain': {'domain': ['www.plosone.org'], 'crossmark-restriction': False}, 'abstract': '<jats:sec id="sec001">\n' '<jats:title>Background</jats:title>\n' '<jats:p>The current situation of the unprecedented COVID-19 pandemic leverages Artificial ' 'Intelligence (AI) as an innovative tool for addressing the evolving clinical challenges. An ' 'example is utilizing Machine Learning (ML) models—a subfield of AI that take advantage of ' 'observational data/Electronic Health Records (EHRs) to support clinical decision-making for ' 'COVID-19 cases. This study aimed to evaluate the clinical characteristics and risk factors ' 'for COVID-19 patients in the United Arab Emirates utilizing EHRs and ML for survival analysis ' 'models.</jats:p>\n' '</jats:sec>\n' '<jats:sec id="sec002">\n' '<jats:title>Methods</jats:title>\n' '<jats:p>We tested various ML models for survival analysis in this work we trained those ' 'models using a different subset of features extracted by several feature selection methods. ' 'Finally, the best model was evaluated and interpreted using goodness-of-fit based on ' 'calibration curves,Partial Dependence Plots and concordance index.</jats:p>\n' '</jats:sec>\n' '<jats:sec id="sec003">\n' '<jats:title>Results</jats:title>\n' '<jats:p>The risk of severe disease increases with elevated levels of C-reactive protein, ' 'ferritin, lactate dehydrogenase, Modified Early Warning Score, respiratory rate and troponin. ' 'The risk also increases with hypokalemia, oxygen desaturation and lower estimated glomerular ' 'filtration rate and hypocalcemia and lymphopenia.</jats:p>\n' '</jats:sec>\n' '<jats:sec id="sec004">\n' '<jats:title>Conclusion</jats:title>\n' '<jats:p>Analyzing clinical data using AI models can provide vital information for clinician ' 'to measure the risk of morbidity and mortality of COVID-19 patients. Further validation is ' 'crucial to implement the model in real clinical settings.</jats:p>\n' '</jats:sec>', 'DOI': '10.1371/journal.pone.0291373', 'type': 'journal-article', 'created': {'date-parts': [[2024, 1, 11]], 'date-time': '2024-01-11T18:23:56Z', 'timestamp': 1704997436000}, 'page': 'e0291373', 'update-policy': 'http://dx.doi.org/10.1371/journal.pone.corrections_policy', 'source': 'Crossref', 'is-referenced-by-count': 0, 'title': 'Utilizing machine learning for survival analysis to identify risk factors for COVID-19 intensive ' 'care unit admission: A retrospective cohort study from the United Arab Emirates', 'prefix': '10.1371', 'volume': '19', 'author': [ { 'ORCID': 'http://orcid.org/0000-0003-1868-1003', 'authenticated-orcid': True, 'given': 'Aamna', 'family': 'AlShehhi', 'sequence': 'first', 'affiliation': []}, {'given': 'Taleb M.', 'family': 'Almansoori', 'sequence': 'additional', 'affiliation': []}, {'given': 'Ahmed R.', 'family': 'Alsuwaidi', 'sequence': 'additional', 'affiliation': []}, { 'ORCID': 'http://orcid.org/0000-0002-9229-8726', 'authenticated-orcid': True, 'given': 'Hiba', 'family': 'Alblooshi', 'sequence': 'additional', 'affiliation': []}], 'member': '340', 'published-online': {'date-parts': [[2024, 1, 11]]}, 'reference': [ { 'issue': '3', 'key': 'pone.0291373.ref001', 'doi-asserted-by': 'crossref', 'first-page': 'e06561', 'DOI': '10.1016/j.heliyon.2021.e06561', 'article-title': 'Clinical characteristics and risk factors for disease severity and ' 'mortality of COVID-19 patients with diabetes mellitus in Kazakhstan: A ' 'nationwide study', 'volume': '7', 'author': 'A. 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