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10.1016/j.compbiomed.2022.105426 http://scihub22266oqcxt.onion/10.1016/j.compbiomed.2022.105426 35569336!8979841!35569336     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 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Comput+Biol+Med 2022 ; 146 (ä): 105426 Nephropedia Template TP {{tp|p=35569336|t=2022. Decoding clinical biomarker space of COVID-19: Exploring matrix factorization-based feature selection methods |pdf=|usr=}}{{35569336}} gab.com Text Decoding clinical biomarker space of COVID-19: Exploring matrix factorization-based feature selection methods JO: Comput Biol Med http://www.kidney.de/pget.php?&tw=1&pmid=35569336 #FOAvip One of the most critical challenges in managing complex diseases like COVID-19 is to establish an intelligent triage system that can optimize the clinical decision-making at the time of a global pandemic. The clinical presentation and patients' characteristics are usually utilized to identify those patients who need more critical care. However, the clinical evidence shows an unmet need to determine more accurate and optimal clinical biomarkers to triage patients under a condition like the COVID-19 crisis. Here we have presented a machine learning approach to find a group of clinical indicators from the blood tests of a set of COVID-19 patients that are predictive of poor prognosis and morbidity. Our approach consists of two interconnected schemes: Feature Selection and Prognosis Classification. The former is based on different Matrix Factorization (MF)-based methods, and the latter is performed using Random Forest algorithm. Our model reveals that Arterial Blood Gas (ABG) O(2) Saturation and C-Reactive Protein (CRP) are the most important clinical biomarkers determining the poor prognosis in these patients. Our approach paves the path of building quantitative and optimized clinical management systems for COVID-19 and similar diseases. Twit Text FOAVip Decoding clinical biomarker space of COVID-19: Exploring matrix factorization-based feature selection methods ????Comput Biol Med http://www.kidney.de/pget.php?&tw=1&pmid=35569336 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=35569336 #FOAvip English Wikipedia <ref>{{Cite pmid|35569336}}</ref> Decoding clinical biomarker space of COVID-19: Exploring matrix factorization-based feature selection methods #MMPMID35569336 Saberi-Movahed F; Mohammadifard M; Mehrpooya A; Rezaei-Ravari M; Berahmand K; Rostami M; Karami S; Najafzadeh M; Hajinezhad D; Jamshidi M; Abedi F; Mohammadifard M; Farbod E; Safavi F; Dorvash M; Mottaghi-Dastjerdi N; Vahedi S; Eftekhari M; Saberi-Movahed F; Alinejad-Rokny H; Band SS; Tavassoly I Comput Biol Med 2022[Jul]; 146 (ä): 105426 PMID35569336show ga One of the most critical challenges in managing complex diseases like COVID-19 is to establish an intelligent triage system that can optimize the clinical decision-making at the time of a global pandemic. The clinical presentation and patients' characteristics are usually utilized to identify those patients who need more critical care. However, the clinical evidence shows an unmet need to determine more accurate and optimal clinical biomarkers to triage patients under a condition like the COVID-19 crisis. Here we have presented a machine learning approach to find a group of clinical indicators from the blood tests of a set of COVID-19 patients that are predictive of poor prognosis and morbidity. Our approach consists of two interconnected schemes: Feature Selection and Prognosis Classification. The former is based on different Matrix Factorization (MF)-based methods, and the latter is performed using Random Forest algorithm. Our model reveals that Arterial Blood Gas (ABG) O(2) Saturation and C-Reactive Protein (CRP) are the most important clinical biomarkers determining the poor prognosis in these patients. Our approach paves the path of building quantitative and optimized clinical management systems for COVID-19 and similar diseases. |*COVID-19[MESH] |Biomarkers[MESH] |Humans[MESH] |Machine Learning[MESH] |Pandemics[MESH]Decoding clinical biomarker space of COVID-19: Exploring matrix facto(epmc).pdf DeepDyve Pubget Overpricing