A patient\u2019s 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\u2019s pathophysiology overtime. Yet many investigators ignore this temporal aspect when modeling patient outcomes, considering only the patient\u2019s current or aggregate state. In this paper, we represent patient risk as a time series. In doing so, patient risk strati\ufb01cation becomes a time-series classi\ufb01cation task. The task differs from most applications of time-series analysis, like speech processing, since the time series itself must \ufb01rst be extracted. Thus, we begin by de\ufb01ning 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 classi\ufb01cation with the goal of identifying high-risk patterns. Weapplytheclassi\ufb01cationtothespeci\ufb01ctaskofidentifyingpatientsatrisk of testing positive for hospital acquired Clostridium dif\ufb01cile. 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 strati\ufb01cation outperforms classi\ufb01ers that consider only a patient\u2019s current state (p<0.05).<\/p>\n","protected":false},"excerpt":{"rendered":"
A patient\u2019s 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\u2019s pathophysiology overtime. Yet many investigators ignore this temporal aspect when modeling patient outcomes, considering only the patient\u2019s current or aggregate state. 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