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10.3389/fpubh.2020.587937 http://scihub22266oqcxt.onion/10.3389/fpubh.2020.587937 33102426!7556112!33102426     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Front+Public+Health 2020 ; 8 (ä): 587937 Nephropedia Template TP {{tp|p=33102426|t=2020. Individual-Level Fatality Prediction of COVID-19 Patients Using AI Methods |pdf=|usr=}}{{33102426}} gab.com Text Individual-Level Fatality Prediction of COVID-19 Patients Using AI Methods JO: Front Public Health http://www.kidney.de/pget.php?&tw=1&pmid=33102426 #FOAvip The global covid-19 pandemic puts great pressure on medical resources worldwide and leads healthcare professionals to question which individuals are in imminent need of care. With appropriate data of each patient, hospitals can heuristically predict whether or not a patient requires immediate care. We adopted a deep learning model to predict fatality of individuals tested positive given the patient's underlying health conditions, age, sex, and other factors. As the allocation of resources toward a vulnerable patient could mean the difference between life and death, a fatality prediction model serves as a valuable tool to healthcare workers in prioritizing resources and hospital space. The models adopted were evaluated and refined using the metrics of accuracy, specificity, and sensitivity. After data preprocessing and training, our model is able to predict whether a covid-19 confirmed patient is likely to be dead or not, given their information and disposition. The metrics between the different models are compared. Results indicate that the deep learning model outperforms other machine learning models to solve this rare event prediction problem. Twit Text FOAVip Individual-Level Fatality Prediction of COVID-19 Patients Using AI Methods ????Front Public Health http://www.kidney.de/pget.php?&tw=1&pmid=33102426 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33102426 #FOAvip English Wikipedia <ref>{{Cite pmid|33102426}}</ref> Individual-Level Fatality Prediction of COVID-19 Patients Using AI Methods #MMPMID33102426 Li Y; Horowitz MA; Liu J; Chew A; Lan H; Liu Q; Sha D; Yang C Front Public Health 2020[]; 8 (ä): 587937 PMID33102426show ga The global covid-19 pandemic puts great pressure on medical resources worldwide and leads healthcare professionals to question which individuals are in imminent need of care. With appropriate data of each patient, hospitals can heuristically predict whether or not a patient requires immediate care. We adopted a deep learning model to predict fatality of individuals tested positive given the patient's underlying health conditions, age, sex, and other factors. As the allocation of resources toward a vulnerable patient could mean the difference between life and death, a fatality prediction model serves as a valuable tool to healthcare workers in prioritizing resources and hospital space. The models adopted were evaluated and refined using the metrics of accuracy, specificity, and sensitivity. After data preprocessing and training, our model is able to predict whether a covid-19 confirmed patient is likely to be dead or not, given their information and disposition. The metrics between the different models are compared. Results indicate that the deep learning model outperforms other machine learning models to solve this rare event prediction problem. |*COVID-19[MESH] |*Pandemics[MESH] |Hospitals[MESH] |Humans[MESH] |Machine Learning[MESH]Individual-Level Fatality Prediction of COVID-19 Patients Using AI Met(epmc).pdf DeepDyve Pubget Overpricing