This paper presents the LIMSI experiments to identify risk factors in patient records, as part of our participation in the 2014 i2b2/UTHealth shared-task. Our approach relies on a CRF system complemented by hand-designed patterns to identify risk factor mentions, and on Weka's OneRule algorithm to compute the temporal attributes of each risk factor after transformation into a single-label classification task. We also used a tailored version of the Heideltime tool to identify temporal expressions and to normalize them according to the document creation time (DCT). Our best submission achieved a global .8451 micro F-measure. We obtained more accurate time attribute predictions for risk factors occurring after the DCT, and for medication before the DCT.