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10.2196/24207 http://scihub22266oqcxt.onion/10.2196/24207 33400679!7842859!33400679     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 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 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       JMIR+Med+Inform 2021 ; 9 (1): e24207 Nephropedia Template TP {{tp|p=33400679|t=2021. Federated Learning of Electronic Health Records to Improve Mortality Prediction in Hospitalized Patients With COVID-19: Machine Learning Approach |pdf=|usr=}}{{33400679}} gab.com Text Federated Learning of Electronic Health Records to Improve Mortality Prediction in Hospitalized Patients With COVID-19: Machine Learning Approach JO: JMIR Med Inform http://www.kidney.de/pget.php?&tw=1&pmid=33400679 #FOAvip BACKGROUND: Machine learning models require large datasets that may be siloed across different health care institutions. Machine learning studies that focus on COVID-19 have been limited to single-hospital data, which limits model generalizability. OBJECTIVE: We aimed to use federated learning, a machine learning technique that avoids locally aggregating raw clinical data across multiple institutions, to predict mortality in hospitalized patients with COVID-19 within 7 days. METHODS: Patient data were collected from the electronic health records of 5 hospitals within the Mount Sinai Health System. Logistic regression with L1 regularization/least absolute shrinkage and selection operator (LASSO) and multilayer perceptron (MLP) models were trained by using local data at each site. We developed a pooled model with combined data from all 5 sites, and a federated model that only shared parameters with a central aggregator. RESULTS: The LASSO(federated) model outperformed the LASSO(local) model at 3 hospitals, and the MLP(federated) model performed better than the MLP(local) model at all 5 hospitals, as determined by the area under the receiver operating characteristic curve. The LASSO(pooled) model outperformed the LASSO(federated) model at all hospitals, and the MLP(federated) model outperformed the MLP(pooled) model at 2 hospitals. CONCLUSIONS: The federated learning of COVID-19 electronic health record data shows promise in developing robust predictive models without compromising patient privacy. Twit Text FOAVip Federated Learning of Electronic Health Records to Improve Mortality Prediction in Hospitalized Patients With COVID-19: Machine Learning Approach ????JMIR Med Inform http://www.kidney.de/pget.php?&tw=1&pmid=33400679 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33400679 #FOAvip English Wikipedia <ref>{{Cite pmid|33400679}}</ref> Federated Learning of Electronic Health Records to Improve Mortality Prediction in Hospitalized Patients With COVID-19: Machine Learning Approach #MMPMID33400679 Vaid A; Jaladanki SK; Xu J; Teng S; Kumar A; Lee S; Somani S; Paranjpe I; De Freitas JK; Wanyan T; Johnson KW; Bicak M; Klang E; Kwon YJ; Costa A; Zhao S; Miotto R; Charney AW; Bottinger E; Fayad ZA; Nadkarni GN; Wang F; Glicksberg BS JMIR Med Inform 2021[Jan]; 9 (1): e24207 PMID33400679show ga BACKGROUND: Machine learning models require large datasets that may be siloed across different health care institutions. Machine learning studies that focus on COVID-19 have been limited to single-hospital data, which limits model generalizability. OBJECTIVE: We aimed to use federated learning, a machine learning technique that avoids locally aggregating raw clinical data across multiple institutions, to predict mortality in hospitalized patients with COVID-19 within 7 days. METHODS: Patient data were collected from the electronic health records of 5 hospitals within the Mount Sinai Health System. Logistic regression with L1 regularization/least absolute shrinkage and selection operator (LASSO) and multilayer perceptron (MLP) models were trained by using local data at each site. We developed a pooled model with combined data from all 5 sites, and a federated model that only shared parameters with a central aggregator. RESULTS: The LASSO(federated) model outperformed the LASSO(local) model at 3 hospitals, and the MLP(federated) model performed better than the MLP(local) model at all 5 hospitals, as determined by the area under the receiver operating characteristic curve. The LASSO(pooled) model outperformed the LASSO(federated) model at all hospitals, and the MLP(federated) model outperformed the MLP(pooled) model at 2 hospitals. CONCLUSIONS: The federated learning of COVID-19 electronic health record data shows promise in developing robust predictive models without compromising patient privacy. äFederated Learning of Electronic Health Records to Improve Mortality P(epmc).pdf DeepDyve Pubget Overpricing