Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText. Kick-your-searchterm to multiple Engines kick-your-query now !>

A dictionary by aggregated review articles of nephrology, medicine and the life sciences Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

10.1038/s41598-021-82885-y http://scihub22266oqcxt.onion/10.1038/s41598-021-82885-y 33558602!7870665!33558602     free     free     free Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Sci+Rep 2021 ; 11 (1): 3343 Nephropedia Template TP {{tp|p=33558602|t=2021. A multipurpose machine learning approach to predict COVID-19 negative prognosis in Sao Paulo, Brazil |pdf=|usr=}}{{33558602}} gab.com Text A multipurpose machine learning approach to predict COVID-19 negative prognosis in Sao Paulo, Brazil JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=33558602 #FOAvip The new coronavirus disease (COVID-19) is a challenge for clinical decision-making and the effective allocation of healthcare resources. An accurate prognostic assessment is necessary to improve survival of patients, especially in developing countries. This study proposes to predict the risk of developing critical conditions in COVID-19 patients by training multipurpose algorithms. We followed a total of 1040 patients with a positive RT-PCR diagnosis for COVID-19 from a large hospital from Sao Paulo, Brazil, from March to June 2020, of which 288 (28%) presented a severe prognosis, i.e. Intensive Care Unit (ICU) admission, use of mechanical ventilation or death. We used routinely-collected laboratory, clinical and demographic data to train five machine learning algorithms (artificial neural networks, extra trees, random forests, catboost, and extreme gradient boosting). We used a random sample of 70% of patients to train the algorithms and 30% were left for performance assessment, simulating new unseen data. In order to assess if the algorithms could capture general severe prognostic patterns, each model was trained by combining two out of three outcomes to predict the other. All algorithms presented very high predictive performance (average AUROC of 0.92, sensitivity of 0.92, and specificity of 0.82). The three most important variables for the multipurpose algorithms were ratio of lymphocyte per C-reactive protein, C-reactive protein and Braden Scale. The results highlight the possibility that machine learning algorithms are able to predict unspecific negative COVID-19 outcomes from routinely-collected data. Twit Text FOAVip A multipurpose machine learning approach to predict COVID-19 negative prognosis in Sao Paulo, Brazil ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=33558602 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33558602 #FOAvip English Wikipedia <ref>{{Cite pmid|33558602}}</ref> A multipurpose machine learning approach to predict COVID-19 negative prognosis in Sao Paulo, Brazil #MMPMID33558602 Fernandes FT; de Oliveira TA; Teixeira CE; Batista AFM; Dalla Costa G; Chiavegatto Filho ADP Sci Rep 2021[Feb]; 11 (1): 3343 PMID33558602show ga The new coronavirus disease (COVID-19) is a challenge for clinical decision-making and the effective allocation of healthcare resources. An accurate prognostic assessment is necessary to improve survival of patients, especially in developing countries. This study proposes to predict the risk of developing critical conditions in COVID-19 patients by training multipurpose algorithms. We followed a total of 1040 patients with a positive RT-PCR diagnosis for COVID-19 from a large hospital from Sao Paulo, Brazil, from March to June 2020, of which 288 (28%) presented a severe prognosis, i.e. Intensive Care Unit (ICU) admission, use of mechanical ventilation or death. We used routinely-collected laboratory, clinical and demographic data to train five machine learning algorithms (artificial neural networks, extra trees, random forests, catboost, and extreme gradient boosting). We used a random sample of 70% of patients to train the algorithms and 30% were left for performance assessment, simulating new unseen data. In order to assess if the algorithms could capture general severe prognostic patterns, each model was trained by combining two out of three outcomes to predict the other. All algorithms presented very high predictive performance (average AUROC of 0.92, sensitivity of 0.92, and specificity of 0.82). The three most important variables for the multipurpose algorithms were ratio of lymphocyte per C-reactive protein, C-reactive protein and Braden Scale. The results highlight the possibility that machine learning algorithms are able to predict unspecific negative COVID-19 outcomes from routinely-collected data. |*Machine Learning[MESH] |Adult[MESH] |Aged[MESH] |Aged, 80 and over[MESH] |Algorithms[MESH] |Brazil/epidemiology[MESH] |C-Reactive Protein/analysis[MESH] |COVID-19/*diagnosis/*epidemiology/mortality/virology[MESH] |Cohort Studies[MESH] |Computational Biology/*methods[MESH] |Female[MESH] |Humans[MESH] |Intensive Care Units[MESH] |Length of Stay[MESH] |Lymphocyte Count[MESH] |Male[MESH] |Middle Aged[MESH] |Prognosis[MESH] |Respiration, Artificial[MESH] |Reverse Transcriptase Polymerase Chain Reaction[MESH]A multipurpose machine learning approach to predict COVID-19 negative (epmc).pdf DeepDyve Pubget Overpricing