About: The myth of generalisability in clinical research and machine learning in health care

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	The myth of generalisability in clinical research and machine learning in health care
Creator	Celi, Leo Doshi-Velez, Finale Futoma, Joseph Panch, Trishan Simons, Morgan Celi, Anthony Doshi-Velez, F Futoma, J Leo, Dr
Source	Elsevier; PMC
abstract	An emphasis on overly broad notions of generalisability as it pertains to applications of machine learning in health care can overlook situations in which machine learning might provide clinical utility. We believe that this narrow focus on generalisability should be replaced with wider considerations for the ultimate goal of building machine learning systems that are useful at the bedside.
has issue date	2020-08-24(xsd:dateTime)
bibo:doi	10.1016/s2589-7500(20)30186-2
has license	no-cc
sha1sum (hex)	a2a39420378fe516af4a9081d7af1646203d341d
schema:url	https://doi.org/10.1016/s2589-7500%2820%2930186-2
resource representing a document's title	The myth of generalisability in clinical research and machine learning in health care
has PubMed Central identifier	PMC7444947
schema:publication	Lancet Digit Health
resource representing a document's body	covid:a2a39420378fe516af4a9081d7af1646203d341d#body_text
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