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10.1016/j.jcrc.2020.10.033 http://scihub22266oqcxt.onion/10.1016/j.jcrc.2020.10.033 33238219!7669246!33238219     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Crit+Care 2021 ; 62 (ä): 25-30 Nephropedia Template TP {{tp|p=33238219|t=2021. Development of a machine learning algorithm to predict intubation among hospitalized patients with COVID-19 |pdf=|usr=}}{{33238219}} gab.com Text Development of a machine learning algorithm to predict intubation among hospitalized patients with COVID-19 JO: J Crit Care http://www.kidney.de/pget.php?&tw=1&pmid=33238219 #FOAvip PURPOSE: The purpose of this study is to develop a machine learning algorithm to predict future intubation among patients diagnosed or suspected with COVID-19. MATERIALS AND METHODS: This is a retrospective cohort study of patients diagnosed or under investigation for COVID-19. A machine learning algorithm was trained to predict future presence of intubation based on prior vitals, laboratory, and demographic data. Model performance was compared to ROX index, a validated prognostic tool for prediction of mechanical ventilation. RESULTS: 4087 patients admitted to five hospitals between February 2020 and April 2020 were included. 11.03% of patients were intubated. The machine learning model outperformed the ROX-index, demonstrating an area under the receiver characteristic curve (AUC) of 0.84 and 0.64, and area under the precision-recall curve (AUPRC) of 0.30 and 0.13, respectively. In the Kaplan-Meier analysis, patients alerted by the model were more likely to require intubation during their admission (p < 0.0001). CONCLUSION: In patients diagnosed or under investigation for COVID-19, machine learning can be used to predict future risk of intubation based on clinical data which are routinely collected and available in clinical setting. Such an approach may facilitate identification of high-risk patients to assist in clinical care. Twit Text FOAVip Development of a machine learning algorithm to predict intubation among hospitalized patients with COVID-19 ????J Crit Care http://www.kidney.de/pget.php?&tw=1&pmid=33238219 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33238219 #FOAvip English Wikipedia <ref>{{Cite pmid|33238219}}</ref> Development of a machine learning algorithm to predict intubation among hospitalized patients with COVID-19 #MMPMID33238219 Arvind V; Kim JS; Cho BH; Geng E; Cho SK J Crit Care 2021[Apr]; 62 (ä): 25-30 PMID33238219show ga PURPOSE: The purpose of this study is to develop a machine learning algorithm to predict future intubation among patients diagnosed or suspected with COVID-19. MATERIALS AND METHODS: This is a retrospective cohort study of patients diagnosed or under investigation for COVID-19. A machine learning algorithm was trained to predict future presence of intubation based on prior vitals, laboratory, and demographic data. Model performance was compared to ROX index, a validated prognostic tool for prediction of mechanical ventilation. RESULTS: 4087 patients admitted to five hospitals between February 2020 and April 2020 were included. 11.03% of patients were intubated. The machine learning model outperformed the ROX-index, demonstrating an area under the receiver characteristic curve (AUC) of 0.84 and 0.64, and area under the precision-recall curve (AUPRC) of 0.30 and 0.13, respectively. In the Kaplan-Meier analysis, patients alerted by the model were more likely to require intubation during their admission (p < 0.0001). CONCLUSION: In patients diagnosed or under investigation for COVID-19, machine learning can be used to predict future risk of intubation based on clinical data which are routinely collected and available in clinical setting. Such an approach may facilitate identification of high-risk patients to assist in clinical care. |*Algorithms[MESH] |*Intubation, Intratracheal[MESH] |*Respiration, Artificial[MESH] |*Supervised Machine Learning[MESH] |Adult[MESH] |Aged[MESH] |Aged, 80 and over[MESH] |Area Under Curve[MESH] |COVID-19/*therapy[MESH] |Cohort Studies[MESH] |Female[MESH] |Hospitalization[MESH] |Humans[MESH] |Male[MESH] |Middle Aged[MESH] |New York City/epidemiology[MESH] |Predictive Value of Tests[MESH] |Prognosis[MESH] |Retrospective Studies[MESH]Development of a machine learning algorithm to predict intubation amon(epmc).pdf DeepDyve Pubget Overpricing