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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
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1 to 1 of 1 Research Studies DisplayedTaylor RA, Pare JR, Venkatesh AK
Prediction of in-hospital mortality in emergency department patients with sepsis: A local big data-driven, machine learning approach.
In this proof-of-concept study, a local, big data-driven, machine learning approach is compared to existing clinical decision rules (CDRs) and traditional analytic methods using the prediction of sepsis in-hospital mortality as the use case. It concluded that this approach outperformed existing CDRs as well as traditional analytic techniques for predicting in-hospital mortality of ED patients with sepsis.
AHRQ-funded; HS021271.
Citation: Taylor RA, Pare JR, Venkatesh AK .
Prediction of in-hospital mortality in emergency department patients with sepsis: A local big data-driven, machine learning approach.
Acad Emerg Med 2016 Mar;23(3):269-78. doi: 10.1111/acem.12876.
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Keywords: Emergency Medical Services (EMS), Mortality, Clinical Decision Support (CDS), Sepsis, Health Information Technology (HIT)