Screening numerous citations for a systematic review can be laborious and costly, with the risk of missing relevant items among the retrieved hits. We shall present the results of recent research, in collaboration with NICE, into how a modest number of decisions by the human analyst can, in a few cycles of interactions, guide a system into reliably distinguishing what should be included and what should be excluded for a review. We shall also show how techniques from text mining can be used to support analysts working on public health reviews with a complex, even fuzzy, evidence base.
21 May 2019
|Title||Public Health Data Science Day|
|Period||21/05/19 → 21/05/19|
|Location||Public Health England|
|Degree of recognition||National event|