Patient Risk Stratiï¬cation for Hospital-Associated C. diff as aTime-Series Classiï¬cation Task - Wiens, Jenna; Guttag, John V.; Horvitz, Eric (NIPS, 2012)
A patient’s risk for adverse events is affected by temporal processes including the nature and timing of diagnostic and therapeutic activities, and the overall evolution of the patient’s pathophysiology overtime. Yet many investigators ignore this temporal aspect when modeling patient outcomes, considering only the patient’s current or aggregate state. In this paper, we represent patient risk as a time series. In doing so, patient risk stratiﬁcation becomes a time-series classiﬁcation task. The task differs from most applications of time-series analysis, like speech processing, since the time series itself must ﬁrst be extracted. Thus, we begin by deﬁning and extracting approximate risk processes, the evolving approximate daily risk of a patient. Once obtained, we use these signals to explore different approaches to time-series classiﬁcation with the goal of identifying high-risk patterns. Weapplytheclassiﬁcationtothespeciﬁctaskofidentifyingpatientsatrisk of testing positive for hospital acquired Clostridium difﬁcile. We achieve an area under the receiver operating characteristic curve of 0.79 on a held-out set of several hundred patients. Our two-stage approach to risk stratiﬁcation outperforms classiﬁers that consider only a patient’s current state (p<0.05).