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10.1371/journal.pone.0245384 http://scihub22266oqcxt.onion/10.1371/journal.pone.0245384 33524039!7850490!33524039     free     free     free Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       PLoS+One 2021 ; 16 (2): e0245384 Nephropedia Template TP {{tp|p=33524039|t=2021. Machine learning model for predicting severity prognosis in patients infected with COVID-19: Study protocol from COVID-AI Brasil |pdf=|usr=}}{{33524039}} gab.com Text Machine learning model for predicting severity prognosis in patients infected with COVID-19: Study protocol from COVID-AI Brasil JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=33524039 #FOAvip The new coronavirus, which began to be called SARS-CoV-2, is a single-stranded RNA beta coronavirus, initially identified in Wuhan (Hubei province, China) and currently spreading across six continents causing a considerable harm to patients, with no specific tools until now to provide prognostic outcomes. Thus, the aim of this study is to evaluate possible findings on chest CT of patients with signs and symptoms of respiratory syndromes and positive epidemiological factors for COVID-19 infection and to correlate them with the course of the disease. In this sense, it is also expected to develop specific machine learning algorithm for this purpose, through pulmonary segmentation, which can predict possible prognostic factors, through more accurate results. Our alternative hypothesis is that the machine learning model based on clinical, radiological and epidemiological data will be able to predict the severity prognosis of patients infected with COVID-19. We will perform a multicenter retrospective longitudinal study to obtain a large number of cases in a short period of time, for better study validation. Our convenience sample (at least 20 cases for each outcome) will be collected in each center considering the inclusion and exclusion criteria. We will evaluate patients who enter the hospital with clinical signs and symptoms of acute respiratory syndrome, from March to May 2020. We will include individuals with signs and symptoms of acute respiratory syndrome, with positive epidemiological history for COVID-19, who have performed a chest computed tomography. We will assess chest CT of these patients and to correlate them with the course of the disease. Primary outcomes:1) Time to hospital discharge; 2) Length of stay in the ICU; 3) orotracheal intubation;4) Development of Acute Respiratory Discomfort Syndrome. Secondary outcomes:1) Sepsis; 2) Hypotension or cardiocirculatory dysfunction requiring the prescription of vasopressors or inotropes; 3) Coagulopathy; 4) Acute Myocardial Infarction; 5) Acute Renal Insufficiency; 6) Death. We will use the AUC and F1-score of these algorithms as the main metrics, and we hope to identify algorithms capable of generalizing their results for each specified primary and secondary outcome. Twit Text FOAVip Machine learning model for predicting severity prognosis in patients infected with COVID-19: Study protocol from COVID-AI Brasil ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=33524039 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33524039 #FOAvip English Wikipedia <ref>{{Cite pmid|33524039}}</ref> Machine learning model for predicting severity prognosis in patients infected with COVID-19: Study protocol from COVID-AI Brasil #MMPMID33524039 Paiva Proenca Lobo Lopes F; Kitamura FC; Prado GF; Kuriki PEA; Garcia MRT PLoS One 2021[]; 16 (2): e0245384 PMID33524039show ga The new coronavirus, which began to be called SARS-CoV-2, is a single-stranded RNA beta coronavirus, initially identified in Wuhan (Hubei province, China) and currently spreading across six continents causing a considerable harm to patients, with no specific tools until now to provide prognostic outcomes. Thus, the aim of this study is to evaluate possible findings on chest CT of patients with signs and symptoms of respiratory syndromes and positive epidemiological factors for COVID-19 infection and to correlate them with the course of the disease. In this sense, it is also expected to develop specific machine learning algorithm for this purpose, through pulmonary segmentation, which can predict possible prognostic factors, through more accurate results. Our alternative hypothesis is that the machine learning model based on clinical, radiological and epidemiological data will be able to predict the severity prognosis of patients infected with COVID-19. We will perform a multicenter retrospective longitudinal study to obtain a large number of cases in a short period of time, for better study validation. Our convenience sample (at least 20 cases for each outcome) will be collected in each center considering the inclusion and exclusion criteria. We will evaluate patients who enter the hospital with clinical signs and symptoms of acute respiratory syndrome, from March to May 2020. We will include individuals with signs and symptoms of acute respiratory syndrome, with positive epidemiological history for COVID-19, who have performed a chest computed tomography. We will assess chest CT of these patients and to correlate them with the course of the disease. Primary outcomes:1) Time to hospital discharge; 2) Length of stay in the ICU; 3) orotracheal intubation;4) Development of Acute Respiratory Discomfort Syndrome. Secondary outcomes:1) Sepsis; 2) Hypotension or cardiocirculatory dysfunction requiring the prescription of vasopressors or inotropes; 3) Coagulopathy; 4) Acute Myocardial Infarction; 5) Acute Renal Insufficiency; 6) Death. We will use the AUC and F1-score of these algorithms as the main metrics, and we hope to identify algorithms capable of generalizing their results for each specified primary and secondary outcome. |*Artificial Intelligence[MESH] |*Machine Learning[MESH] |*Severity of Illness Index[MESH] |Algorithms[MESH] |Brazil[MESH] |COVID-19/*diagnosis/*pathology[MESH] |Humans[MESH] |Prognosis[MESH]Machine learning model for predicting severity prognosis in patients i(epmc).pdf DeepDyve Pubget Overpricing