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10.1038/s41746-020-0295-6 http://scihub22266oqcxt.onion/10.1038/s41746-020-0295-6 32577534!7305156!32577534     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       NPJ+Digit+Med 2020 ; 3 (ä): 87 Nephropedia Template TP {{tp|p=32577534|t=2020. "Yes, but will it work for my patients?" Driving clinically relevant research with benchmark datasets |pdf=|usr=}}{{32577534}} gab.com Text "Yes, but will it work for my patients " Driving clinically relevant research with benchmark datasets JO: NPJ Digit Med http://www.kidney.de/pget.php?&tw=1&pmid=32577534 #FOAvip Benchmark datasets have a powerful normative influence: by determining how the real world is represented in data, they define which problems will first be solved by algorithms built using the datasets and, by extension, who these algorithms will work for. It is desirable for these datasets to serve four functions: (1) enabling the creation of clinically relevant algorithms; (2) facilitating like-for-like comparison of algorithmic performance; (3) ensuring reproducibility of algorithms; (4) asserting a normative influence on the clinical domains and diversity of patients that will potentially benefit from technological advances. Without benchmark datasets that satisfy these functions, it is impossible to address two perennial concerns of clinicians experienced in computational research: "the data scientists just go where the data is rather than where the needs are," and, "yes, but will this work for my patients?" If algorithms are to be developed and applied for the care of patients, then it is prudent for the research community to create benchmark datasets proactively, across specialties. As yet, best practice in this area has not been defined. Broadly speaking, efforts will include design of the dataset; compliance and contracting issues relating to the sharing of sensitive data; enabling access and reuse; and planning for translation of algorithms to the clinical environment. If a deliberate and systematic approach is not followed, not only will the considerable benefits of clinical algorithms fail to be realized, but the potential harms may be regressively incurred across existing gradients of social inequity. Twit Text FOAVip "Yes, but will it work for my patients " Driving clinically relevant research with benchmark datasets ????NPJ Digit Med http://www.kidney.de/pget.php?&tw=1&pmid=32577534 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32577534 #FOAvip English Wikipedia <ref>{{Cite pmid|32577534}}</ref> "Yes, but will it work for my patients?" Driving clinically relevant research with benchmark datasets #MMPMID32577534 Panch T; Pollard TJ; Mattie H; Lindemer E; Keane PA; Celi LA NPJ Digit Med 2020[]; 3 (ä): 87 PMID32577534show ga Benchmark datasets have a powerful normative influence: by determining how the real world is represented in data, they define which problems will first be solved by algorithms built using the datasets and, by extension, who these algorithms will work for. It is desirable for these datasets to serve four functions: (1) enabling the creation of clinically relevant algorithms; (2) facilitating like-for-like comparison of algorithmic performance; (3) ensuring reproducibility of algorithms; (4) asserting a normative influence on the clinical domains and diversity of patients that will potentially benefit from technological advances. Without benchmark datasets that satisfy these functions, it is impossible to address two perennial concerns of clinicians experienced in computational research: "the data scientists just go where the data is rather than where the needs are," and, "yes, but will this work for my patients?" If algorithms are to be developed and applied for the care of patients, then it is prudent for the research community to create benchmark datasets proactively, across specialties. As yet, best practice in this area has not been defined. Broadly speaking, efforts will include design of the dataset; compliance and contracting issues relating to the sharing of sensitive data; enabling access and reuse; and planning for translation of algorithms to the clinical environment. If a deliberate and systematic approach is not followed, not only will the considerable benefits of clinical algorithms fail to be realized, but the potential harms may be regressively incurred across existing gradients of social inequity. ä"Yes, but will it work for my patients?" Driving clinically relevant r(epmc).pdf DeepDyve Pubget Overpricing