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10.1371/journal.pone.0239474 http://scihub22266oqcxt.onion/10.1371/journal.pone.0239474 32960917!7508387!32960917     free     free     free Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       PLoS+One 2020 ; 15 (9): e0239474 Nephropedia Template TP {{tp|p=32960917|t=2020. A machine learning algorithm to increase COVID-19 inpatient diagnostic capacity |pdf=|usr=}}{{32960917}} gab.com Text A machine learning algorithm to increase COVID-19 inpatient diagnostic capacity JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=32960917 #FOAvip Worldwide, testing capacity for SARS-CoV-2 is limited and bottlenecks in the scale up of polymerase chain reaction (PCR-based testing exist. Our aim was to develop and evaluate a machine learning algorithm to diagnose COVID-19 in the inpatient setting. The algorithm was based on basic demographic and laboratory features to serve as a screening tool at hospitals where testing is scarce or unavailable. We used retrospectively collected data from the UCLA Health System in Los Angeles, California. We included all emergency room or inpatient cases receiving SARS-CoV-2 PCR testing who also had a set of ancillary laboratory features (n = 1,455) between 1 March 2020 and 24 May 2020. We tested seven machine learning models and used a combination of those models for the final diagnostic classification. In the test set (n = 392), our combined model had an area under the receiver operator curve of 0.91 (95% confidence interval 0.87-0.96). The model achieved a sensitivity of 0.93 (95% CI 0.85-0.98), specificity of 0.64 (95% CI 0.58-0.69). We found that our machine learning algorithm had excellent diagnostic metrics compared to SARS-CoV-2 PCR. This ensemble machine learning algorithm to diagnose COVID-19 has the potential to be used as a screening tool in hospital settings where PCR testing is scarce or unavailable. Twit Text FOAVip A machine learning algorithm to increase COVID-19 inpatient diagnostic capacity ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=32960917 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32960917 #FOAvip English Wikipedia <ref>{{Cite pmid|32960917}}</ref> A machine learning algorithm to increase COVID-19 inpatient diagnostic capacity #MMPMID32960917 Goodman-Meza D; Rudas A; Chiang JN; Adamson PC; Ebinger J; Sun N; Botting P; Fulcher JA; Saab FG; Brook R; Eskin E; An U; Kordi M; Jew B; Balliu B; Chen Z; Hill BL; Rahmani E; Halperin E; Manuel V PLoS One 2020[]; 15 (9): e0239474 PMID32960917show ga Worldwide, testing capacity for SARS-CoV-2 is limited and bottlenecks in the scale up of polymerase chain reaction (PCR-based testing exist. Our aim was to develop and evaluate a machine learning algorithm to diagnose COVID-19 in the inpatient setting. The algorithm was based on basic demographic and laboratory features to serve as a screening tool at hospitals where testing is scarce or unavailable. We used retrospectively collected data from the UCLA Health System in Los Angeles, California. We included all emergency room or inpatient cases receiving SARS-CoV-2 PCR testing who also had a set of ancillary laboratory features (n = 1,455) between 1 March 2020 and 24 May 2020. We tested seven machine learning models and used a combination of those models for the final diagnostic classification. In the test set (n = 392), our combined model had an area under the receiver operator curve of 0.91 (95% confidence interval 0.87-0.96). The model achieved a sensitivity of 0.93 (95% CI 0.85-0.98), specificity of 0.64 (95% CI 0.58-0.69). We found that our machine learning algorithm had excellent diagnostic metrics compared to SARS-CoV-2 PCR. This ensemble machine learning algorithm to diagnose COVID-19 has the potential to be used as a screening tool in hospital settings where PCR testing is scarce or unavailable. |*Betacoronavirus[MESH] |*Inpatients[MESH] |*Machine Learning[MESH] |Adult[MESH] |Aged[MESH] |Area Under Curve[MESH] |COVID-19[MESH] |COVID-19 Testing[MESH] |Clinical Laboratory Techniques/*methods/standards[MESH] |Coronavirus Infections/*diagnosis[MESH] |Humans[MESH] |Los Angeles[MESH] |Mass Screening/methods/standards[MESH] |Middle Aged[MESH] |Pandemics[MESH] |Pneumonia, Viral/*diagnosis[MESH] |Polymerase Chain Reaction[MESH] |Retrospective Studies[MESH]A machine learning algorithm to increase COVID-19 inpatient diagnostic(epmc).pdf DeepDyve Pubget Overpricing