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10.1093/jamia/ocaa258 http://scihub22266oqcxt.onion/10.1093/jamia/ocaa258 33027509!7797735!33027509     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Am+Med+Inform+Assoc 2021 ; 28 (2): 360-364 Nephropedia Template TP {{tp|p=33027509|t=2021. Potential limitations in COVID-19 machine learning due to data source variability: A case study in the nCov2019 dataset |pdf=|usr=}}{{33027509}} gab.com Text Potential limitations in COVID-19 machine learning due to data source variability: A case study in the nCov2019 dataset JO: J Am Med Inform Assoc http://www.kidney.de/pget.php?&tw=1&pmid=33027509 #FOAvip OBJECTIVE: The lack of representative coronavirus disease 2019 (COVID-19) data is a bottleneck for reliable and generalizable machine learning. Data sharing is insufficient without data quality, in which source variability plays an important role. We showcase and discuss potential biases from data source variability for COVID-19 machine learning. MATERIALS AND METHODS: We used the publicly available nCov2019 dataset, including patient-level data from several countries. We aimed to the discovery and classification of severity subgroups using symptoms and comorbidities. RESULTS: Cases from the 2 countries with the highest prevalence were divided into separate subgroups with distinct severity manifestations. This variability can reduce the representativeness of training data with respect the model target populations and increase model complexity at risk of overfitting. CONCLUSIONS: Data source variability is a potential contributor to bias in distributed research networks. We call for systematic assessment and reporting of data source variability and data quality in COVID-19 data sharing, as key information for reliable and generalizable machine learning. Twit Text FOAVip Potential limitations in COVID-19 machine learning due to data source variability: A case study in the nCov2019 dataset ????J Am Med Inform Assoc http://www.kidney.de/pget.php?&tw=1&pmid=33027509 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33027509 #FOAvip English Wikipedia <ref>{{Cite pmid|33027509}}</ref> Potential limitations in COVID-19 machine learning due to data source variability: A case study in the nCov2019 dataset #MMPMID33027509 Saez C; Romero N; Conejero JA; Garcia-Gomez JM J Am Med Inform Assoc 2021[Feb]; 28 (2): 360-364 PMID33027509show ga OBJECTIVE: The lack of representative coronavirus disease 2019 (COVID-19) data is a bottleneck for reliable and generalizable machine learning. Data sharing is insufficient without data quality, in which source variability plays an important role. We showcase and discuss potential biases from data source variability for COVID-19 machine learning. MATERIALS AND METHODS: We used the publicly available nCov2019 dataset, including patient-level data from several countries. We aimed to the discovery and classification of severity subgroups using symptoms and comorbidities. RESULTS: Cases from the 2 countries with the highest prevalence were divided into separate subgroups with distinct severity manifestations. This variability can reduce the representativeness of training data with respect the model target populations and increase model complexity at risk of overfitting. CONCLUSIONS: Data source variability is a potential contributor to bias in distributed research networks. We call for systematic assessment and reporting of data source variability and data quality in COVID-19 data sharing, as key information for reliable and generalizable machine learning. |*COVID-19/classification[MESH] |*Data Accuracy[MESH] |*Datasets as Topic/standards[MESH] |*Information Dissemination[MESH] |*Machine Learning[MESH] |Adult[MESH] |Aged[MESH] |Computer Communication Networks[MESH] |Female[MESH] |Humans[MESH] |Male[MESH] |Middle Aged[MESH]Potential limitations in COVID-19 machine learning due to data source (epmc).pdf DeepDyve Pubget Overpricing