Contextual Motifs

Abstract

Motifs are a powerful tool for analyzing physiological waveform data. Standard motif methods, however, ignore important contextual information (e.g., what the patient was doing at the time the data were collected). We hypothesize that these additional contextual data could increase the utility of motifs. us, we propose an extension to motifs, contextual motifs, that incorporates context. Recognizing that, o entimes, context may be unobserved or unavailable, we focus on methods to jointly infer motifs and context. Applied to both simulated and real physiological data, our proposed approach improves upon existing motif methods in terms of the discriminative utility of the discovered motifs. In particular, we discovered contextual motifs in continuous glucose monitor (CGM) data collected from patients with type 1 diabetes. Compared to their contextless counterparts, these contextual motifs led to be er predictions of hypo-and hyperglycemic events. Our results suggest that even when inferred, context is useful in both a long-and short-term prediction horizon when processing and interpreting physiological waveform data.

An analysis of emotion-exchange motifs in multiplex networks during emergency events

“…In Fox et al (2017), Fox et al propose contextual motifs as an extension to network motifs that considers the context under which a particular motif occurs to better predict hypo-and hyperglycemic events in waveform patient data. Their findings suggested that the discovery of such contextual motifs are clinically more meaningful than the contextless counterparts and lead to a more fine-grained interpretation of their meaning.…”