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10.1097/MD.0000000000026532 http://scihub22266oqcxt.onion/10.1097/MD.0000000000026532 34260529!8284724!34260529     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 284.79999999999995 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 284.79999999999995 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 284.79999999999995 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 284.79999999999995 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Medicine+(Baltimore) 2021 ; 100 (28): e26532 Nephropedia Template TP {{tp|p=34260529|t=2021. An artificial neural network model to predict the mortality of COVID-19 patients using routine blood samples at the time of hospital admission: Development and validation study |pdf=|usr=}}{{34260529}} gab.com Text An artificial neural network model to predict the mortality of COVID-19 patients using routine blood samples at the time of hospital admission: Development and validation study JO: Medicine (Baltimore) http://www.kidney.de/pget.php?&tw=1&pmid=34260529 #FOAvip BACKGROUND: In a pandemic situation (e.g., COVID-19), the most important issue is to select patients at risk of high mortality at an early stage and to provide appropriate treatments. However, a few studies applied the model to predict in-hospital mortality using routine blood samples at the time of hospital admission. This study aimed to develop an app, name predict the mortality of COVID-19 patients (PMCP) app, to predict the mortality of COVID-19 patients at hospital-admission time. METHODS: We downloaded patient records from 2 studies, including 361 COVID-19 patients in Wuhan, China, and 106 COVID-19 patients in 3 Korean medical institutions. A total of 30 feature variables were retrieved, consisting of 28 blood biomarkers and 2 demographic variables (i.e., age and gender) of patients. Two models, namely, artificial neural network (ANN) and convolutional neural network (CNN), were compared with each other across 2 scenarios using: 1. raw laboratory versus normalized data and 2. training vs testing datasets (n = 361 and n = 106/361 congruent with30%) to verify the model performance (e.g., sensitivity [SENS], specificity [SPEC], and area under the receiver operating characteristic curve [AUC]). An app for predicting the mortality of COVID-19 patients was developed using the model's estimated parameters for the prediction and classification of PMCP at an earlier stage. Feature variables and prediction results were visualized using the forest plot and category probability curves shown on Google Maps. RESULTS: We observed that: 1. the normalized dataset gains a relatively higher AUC(>0.9) when compared to that(<0.9) in the raw-laboratory dataset based on training data, 2. the normalized dataset in ANN yielded a high AUC of 0.96 that that(=0.91) in CNN based on testing data, and 3. a ready and available app, where anyone can access the model to predict mortality, for PMCP was developed in this study. CONCLUSIONS: Our new PMCP app with ANN model accurately predicts the mortality probability for COVID-19 patients. It is publicly available and aims to help health care providers fight COVID-19 and improve patients' classifications against treatment risk. Twit Text FOAVip An artificial neural network model to predict the mortality of COVID-19 patients using routine blood samples at the time of hospital admission: Development and validation study ????Medicine (Baltimore) http://www.kidney.de/pget.php?&tw=1&pmid=34260529 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34260529 #FOAvip English Wikipedia <ref>{{Cite pmid|34260529}}</ref> An artificial neural network model to predict the mortality of COVID-19 patients using routine blood samples at the time of hospital admission: Development and validation study #MMPMID34260529 Lin JK; Chien TW; Wang LY; Chou W Medicine (Baltimore) 2021[Jul]; 100 (28): e26532 PMID34260529show ga BACKGROUND: In a pandemic situation (e.g., COVID-19), the most important issue is to select patients at risk of high mortality at an early stage and to provide appropriate treatments. However, a few studies applied the model to predict in-hospital mortality using routine blood samples at the time of hospital admission. This study aimed to develop an app, name predict the mortality of COVID-19 patients (PMCP) app, to predict the mortality of COVID-19 patients at hospital-admission time. METHODS: We downloaded patient records from 2 studies, including 361 COVID-19 patients in Wuhan, China, and 106 COVID-19 patients in 3 Korean medical institutions. A total of 30 feature variables were retrieved, consisting of 28 blood biomarkers and 2 demographic variables (i.e., age and gender) of patients. Two models, namely, artificial neural network (ANN) and convolutional neural network (CNN), were compared with each other across 2 scenarios using: 1. raw laboratory versus normalized data and 2. training vs testing datasets (n = 361 and n = 106/361 congruent with30%) to verify the model performance (e.g., sensitivity [SENS], specificity [SPEC], and area under the receiver operating characteristic curve [AUC]). An app for predicting the mortality of COVID-19 patients was developed using the model's estimated parameters for the prediction and classification of PMCP at an earlier stage. Feature variables and prediction results were visualized using the forest plot and category probability curves shown on Google Maps. RESULTS: We observed that: 1. the normalized dataset gains a relatively higher AUC(>0.9) when compared to that(<0.9) in the raw-laboratory dataset based on training data, 2. the normalized dataset in ANN yielded a high AUC of 0.96 that that(=0.91) in CNN based on testing data, and 3. a ready and available app, where anyone can access the model to predict mortality, for PMCP was developed in this study. CONCLUSIONS: Our new PMCP app with ANN model accurately predicts the mortality probability for COVID-19 patients. It is publicly available and aims to help health care providers fight COVID-19 and improve patients' classifications against treatment risk. |*Hospitalization[MESH] |*Neural Networks, Computer[MESH] |Biomarkers/blood[MESH] |COVID-19/*blood/*mortality[MESH] |China/epidemiology[MESH] |Female[MESH] |Humans[MESH] |Male[MESH] |Predictive Value of Tests[MESH] |Prognosis[MESH] |Republic of Korea/epidemiology[MESH]An artificial neural network model to predict the mortality of COVID-1(epmc).pdf DeepDyve Pubget Overpricing