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10.2196/20259 http://scihub22266oqcxt.onion/10.2196/20259 32735549!7451108!32735549     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 Deprecated: Implicit conversion from float 245.2 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+Med+Internet+Res 2020 ; 22 (8): e20259 Nephropedia Template TP {{tp|p=32735549|t=2020. Prognostic Modeling of COVID-19 Using Artificial Intelligence in the United Kingdom: Model Development and Validation |pdf=|usr=}}{{32735549}} gab.com Text Prognostic Modeling of COVID-19 Using Artificial Intelligence in the United Kingdom: Model Development and Validation JO: J Med Internet Res http://www.kidney.de/pget.php?&tw=1&pmid=32735549 #FOAvip BACKGROUND: The current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak is a public health emergency and the case fatality rate in the United Kingdom is significant. Although there appear to be several early predictors of outcome, there are no currently validated prognostic models or scoring systems applicable specifically to patients with confirmed SARS-CoV-2. OBJECTIVE: We aim to create a point-of-admission mortality risk scoring system using an artificial neural network (ANN). METHODS: We present an ANN that can provide a patient-specific, point-of-admission mortality risk prediction to inform clinical management decisions at the earliest opportunity. The ANN analyzes a set of patient features including demographics, comorbidities, smoking history, and presenting symptoms and predicts patient-specific mortality risk during the current hospital admission. The model was trained and validated on data extracted from 398 patients admitted to hospital with a positive real-time reverse transcription polymerase chain reaction (RT-PCR) test for SARS-CoV-2. RESULTS: Patient-specific mortality was predicted with 86.25% accuracy, with a sensitivity of 87.50% (95% CI 61.65%-98.45%) and specificity of 85.94% (95% CI 74.98%-93.36%). The positive predictive value was 60.87% (95% CI 45.23%-74.56%), and the negative predictive value was 96.49% (95% CI 88.23%-99.02%). The area under the receiver operating characteristic curve was 90.12%. CONCLUSIONS: This analysis demonstrates an adaptive ANN trained on data at a single site, which demonstrates the early utility of deep learning approaches in a rapidly evolving pandemic with no established or validated prognostic scoring systems. Twit Text FOAVip Prognostic Modeling of COVID-19 Using Artificial Intelligence in the United Kingdom: Model Development and Validation ????J Med Internet Res http://www.kidney.de/pget.php?&tw=1&pmid=32735549 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32735549 #FOAvip English Wikipedia <ref>{{Cite pmid|32735549}}</ref> Prognostic Modeling of COVID-19 Using Artificial Intelligence in the United Kingdom: Model Development and Validation #MMPMID32735549 Abdulaal A; Patel A; Charani E; Denny S; Mughal N; Moore L J Med Internet Res 2020[Aug]; 22 (8): e20259 PMID32735549show ga BACKGROUND: The current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak is a public health emergency and the case fatality rate in the United Kingdom is significant. Although there appear to be several early predictors of outcome, there are no currently validated prognostic models or scoring systems applicable specifically to patients with confirmed SARS-CoV-2. OBJECTIVE: We aim to create a point-of-admission mortality risk scoring system using an artificial neural network (ANN). METHODS: We present an ANN that can provide a patient-specific, point-of-admission mortality risk prediction to inform clinical management decisions at the earliest opportunity. The ANN analyzes a set of patient features including demographics, comorbidities, smoking history, and presenting symptoms and predicts patient-specific mortality risk during the current hospital admission. The model was trained and validated on data extracted from 398 patients admitted to hospital with a positive real-time reverse transcription polymerase chain reaction (RT-PCR) test for SARS-CoV-2. RESULTS: Patient-specific mortality was predicted with 86.25% accuracy, with a sensitivity of 87.50% (95% CI 61.65%-98.45%) and specificity of 85.94% (95% CI 74.98%-93.36%). The positive predictive value was 60.87% (95% CI 45.23%-74.56%), and the negative predictive value was 96.49% (95% CI 88.23%-99.02%). The area under the receiver operating characteristic curve was 90.12%. CONCLUSIONS: This analysis demonstrates an adaptive ANN trained on data at a single site, which demonstrates the early utility of deep learning approaches in a rapidly evolving pandemic with no established or validated prognostic scoring systems. |*Betacoronavirus[MESH] |*Coronavirus Infections/diagnosis/epidemiology[MESH] |*Pandemics[MESH] |*Pneumonia, Viral/diagnosis/epidemiology[MESH] |Aged[MESH] |Aged, 80 and over[MESH] |Artificial Intelligence[MESH] |COVID-19[MESH] |Comorbidity[MESH] |Female[MESH] |Hospitalization[MESH] |Humans[MESH] |Male[MESH] |Middle Aged[MESH] |Neural Networks, Computer[MESH] |Prognosis[MESH] |ROC Curve[MESH] |SARS-CoV-2[MESH]Prognostic Modeling of COVID-19 Using Artificial Intelligence in the U(epmc).pdf DeepDyve Pubget Overpricing