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10.2196/25988 http://scihub22266oqcxt.onion/10.2196/25988 33872186!8163491!33872186     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=33872186&cmd=llinks): Failed to open stream: HTTP request failed! 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Using Unsupervised Machine Learning to Identify Age- and Sex-Independent Severity Subgroups Among Patients with COVID-19: Observational Longitudinal Study |pdf=|usr=}}{{33872186}} gab.com Text Using Unsupervised Machine Learning to Identify Age- and Sex-Independent Severity Subgroups Among Patients with COVID-19: Observational Longitudinal Study JO: J Med Internet Res http://www.kidney.de/pget.php?&tw=1&pmid=33872186 #FOAvip BACKGROUND: Early detection and intervention are the key factors for improving outcomes in patients with COVID-19. OBJECTIVE: The objective of this observational longitudinal study was to identify nonoverlapping severity subgroups (ie, clusters) among patients with COVID-19, based exclusively on clinical data and standard laboratory tests obtained during patient assessment in the emergency department. METHODS: We applied unsupervised machine learning to a data set of 853 patients with COVID-19 from the HM group of hospitals (HM Hospitales) in Madrid, Spain. Age and sex were not considered while building the clusters, as these variables could introduce biases in machine learning algorithms and raise ethical implications or enable discrimination in triage protocols. RESULTS: From 850 clinical and laboratory variables, four tests-the serum levels of aspartate transaminase (AST), lactate dehydrogenase (LDH), C-reactive protein (CRP), and the number of neutrophils-were enough to segregate the entire patient pool into three separate clusters. Further, the percentage of monocytes and lymphocytes and the levels of alanine transaminase (ALT) distinguished cluster 3 patients from the other two clusters. The highest proportion of deceased patients; the highest levels of AST, ALT, LDH, and CRP; the highest number of neutrophils; and the lowest percentages of monocytes and lymphocytes characterized cluster 1. Cluster 2 included a lower proportion of deceased patients and intermediate levels of the previous laboratory tests. The lowest proportion of deceased patients; the lowest levels of AST, ALT, LDH, and CRP; the lowest number of neutrophils; and the highest percentages of monocytes and lymphocytes characterized cluster 3. CONCLUSIONS: A few standard laboratory tests, deemed available in all emergency departments, have shown good discriminative power for the characterization of severity subgroups among patients with COVID-19. Twit Text FOAVip Using Unsupervised Machine Learning to Identify Age- and Sex-Independent Severity Subgroups Among Patients with COVID-19: Observational Longitudinal Study ????J Med Internet Res http://www.kidney.de/pget.php?&tw=1&pmid=33872186 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33872186 #FOAvip English Wikipedia <ref>{{Cite pmid|33872186}}</ref> Using Unsupervised Machine Learning to Identify Age- and Sex-Independent Severity Subgroups Among Patients with COVID-19: Observational Longitudinal Study #MMPMID33872186 Benito-Leon J; Del Castillo MD; Estirado A; Ghosh R; Dubey S; Serrano JI J Med Internet Res 2021[May]; 23 (5): e25988 PMID33872186show ga BACKGROUND: Early detection and intervention are the key factors for improving outcomes in patients with COVID-19. OBJECTIVE: The objective of this observational longitudinal study was to identify nonoverlapping severity subgroups (ie, clusters) among patients with COVID-19, based exclusively on clinical data and standard laboratory tests obtained during patient assessment in the emergency department. METHODS: We applied unsupervised machine learning to a data set of 853 patients with COVID-19 from the HM group of hospitals (HM Hospitales) in Madrid, Spain. Age and sex were not considered while building the clusters, as these variables could introduce biases in machine learning algorithms and raise ethical implications or enable discrimination in triage protocols. RESULTS: From 850 clinical and laboratory variables, four tests-the serum levels of aspartate transaminase (AST), lactate dehydrogenase (LDH), C-reactive protein (CRP), and the number of neutrophils-were enough to segregate the entire patient pool into three separate clusters. Further, the percentage of monocytes and lymphocytes and the levels of alanine transaminase (ALT) distinguished cluster 3 patients from the other two clusters. The highest proportion of deceased patients; the highest levels of AST, ALT, LDH, and CRP; the highest number of neutrophils; and the lowest percentages of monocytes and lymphocytes characterized cluster 1. Cluster 2 included a lower proportion of deceased patients and intermediate levels of the previous laboratory tests. The lowest proportion of deceased patients; the lowest levels of AST, ALT, LDH, and CRP; the lowest number of neutrophils; and the highest percentages of monocytes and lymphocytes characterized cluster 3. CONCLUSIONS: A few standard laboratory tests, deemed available in all emergency departments, have shown good discriminative power for the characterization of severity subgroups among patients with COVID-19. |*Unsupervised Machine Learning[MESH] |Alanine Transaminase/blood[MESH] |Aspartate Aminotransferases/blood[MESH] |C-Reactive Protein/analysis[MESH] |COVID-19/*diagnosis/*epidemiology/mortality[MESH] |Cell Count[MESH] |Cluster Analysis[MESH] |Datasets as Topic[MESH] |Emergency Service, Hospital[MESH] |Humans[MESH] |L-Lactate Dehydrogenase/blood[MESH] |Longitudinal Studies[MESH] |Lymphocytes[MESH] |Monocytes[MESH] |Neutrophils[MESH] |Prognosis[MESH] |Spain/epidemiology[MESH]Using Unsupervised Machine Learning to Identify Age- and Sex-Independe(epmc).pdf DeepDyve Pubget Overpricing