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10.1186/s12911-021-01537-3 http://scihub22266oqcxt.onion/10.1186/s12911-021-01537-3 34044839!8159067!34044839     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       BMC+Med+Inform+Decis+Mak 2021 ; 21 (1): 172 Nephropedia Template TP {{tp|p=34044839|t=2021. Maintaining proper health records improves machine learning predictions for novel 2019-nCoV |pdf=|usr=}}{{34044839}} gab.com Text Maintaining proper health records improves machine learning predictions for novel 2019-nCoV JO: BMC Med Inform Decis Mak http://www.kidney.de/pget.php?&tw=1&pmid=34044839 #FOAvip BACKGROUND: An ongoing outbreak of a novel coronavirus (2019-nCoV) pneumonia continues to affect the whole world including major countries such as China, USA, Italy, France and the United Kingdom. We present outcome ('recovered', 'isolated' or 'death') risk estimates of 2019-nCoV over 'early' datasets. A major consideration is the likelihood of death for patients with 2019-nCoV. METHOD: Accounting for the impact of the variations in the reporting rate of 2019-nCoV, we used machine learning techniques (AdaBoost, bagging, extra-trees, decision trees and k-nearest neighbour classifiers) on two 2019-nCoV datasets obtained from Kaggle on March 30, 2020. We used 'country', 'age' and 'gender' as features to predict outcome for both datasets. We included the patient's 'disease' history (only present in the second dataset) to predict the outcome for the second dataset. RESULTS: The use of a patient's 'disease' history improves the prediction of 'death' by more than sevenfold. The models ignoring a patent's 'disease' history performed poorly in test predictions. CONCLUSION: Our findings indicate the potential of using a patient's 'disease' history as part of the feature set in machine learning techniques to improve 2019-nCoV predictions. This development can have a positive effect on predictive patient treatment and can result in easing currently overburdened healthcare systems worldwide, especially with the increasing prevalence of second and third wave re-infections in some countries. Twit Text FOAVip Maintaining proper health records improves machine learning predictions for novel 2019-nCoV ????BMC Med Inform Decis Mak http://www.kidney.de/pget.php?&tw=1&pmid=34044839 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34044839 #FOAvip English Wikipedia <ref>{{Cite pmid|34044839}}</ref> Maintaining proper health records improves machine learning predictions for novel 2019-nCoV #MMPMID34044839 Khan K; Ramsahai E BMC Med Inform Decis Mak 2021[May]; 21 (1): 172 PMID34044839show ga BACKGROUND: An ongoing outbreak of a novel coronavirus (2019-nCoV) pneumonia continues to affect the whole world including major countries such as China, USA, Italy, France and the United Kingdom. We present outcome ('recovered', 'isolated' or 'death') risk estimates of 2019-nCoV over 'early' datasets. A major consideration is the likelihood of death for patients with 2019-nCoV. METHOD: Accounting for the impact of the variations in the reporting rate of 2019-nCoV, we used machine learning techniques (AdaBoost, bagging, extra-trees, decision trees and k-nearest neighbour classifiers) on two 2019-nCoV datasets obtained from Kaggle on March 30, 2020. We used 'country', 'age' and 'gender' as features to predict outcome for both datasets. We included the patient's 'disease' history (only present in the second dataset) to predict the outcome for the second dataset. RESULTS: The use of a patient's 'disease' history improves the prediction of 'death' by more than sevenfold. The models ignoring a patent's 'disease' history performed poorly in test predictions. CONCLUSION: Our findings indicate the potential of using a patient's 'disease' history as part of the feature set in machine learning techniques to improve 2019-nCoV predictions. This development can have a positive effect on predictive patient treatment and can result in easing currently overburdened healthcare systems worldwide, especially with the increasing prevalence of second and third wave re-infections in some countries. |*COVID-19[MESH] |*SARS-CoV-2[MESH] |China[MESH] |France[MESH] |Humans[MESH] |Italy[MESH] |Machine Learning[MESH]Maintaining proper health records improves machine learning prediction(epmc).pdf DeepDyve Pubget Overpricing