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Big data to the rescue of systemic inflammatory response syndrome: is electronic data mining the way of the future?

   
@article{ATM12902,
	author = {Utsav Nandi and Michael A. Puskaric and Alan E. Jones},
	title = {Big data to the rescue of systemic inflammatory response syndrome: is electronic data mining the way of the future?},
	journal = {Annals of Translational Medicine},
	volume = {4},
	number = {23},
	year = {2016},
	keywords = {},
	abstract = {The recognition and accurate diagnosis of sepsis continues to stifle clinicians and researchers alike, as evidenced in part by the recently proposed and ever-evolving clinical definitions (1). Based on available data, global estimates of the burden of sepsis are at 31.5 million cases annually with mortality rates around 20% (2,3). Despite decades of research, clinically efficacious therapies remain elusive. To aid the efforts of researchers and clinicians, the ACCP/ SCCM consensus conference in 1992 proposed a set of standard definitions, which have since come to be the cornerstone of clinical sepsis research and influenced clinical practice—enter the systemic inflammatory response syndrome (SIRS) criteria (4).},
	issn = {2305-5847},	url = {https://atm.amegroups.org/article/view/12902}
}