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 over time. 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.

Suddenly fruitful after years of sparse adoption, the long-awaited flowering of artificial intelligence (AI) and machine learning is upon us. Risk management and compliance leaders can expect these advanced analytic technologies will propel productivity-enhancing applications for years to come. But how did we get to this point? And as we enter the third decade of the 21st century, what can we anticipate right around the corner? AI owes its recent gains largely to the accumulation of big data assets and the continually declining cost of computing.

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 over time. Yet many investigators ignore this temporal aspect when modeling patient risk, considering only the patient's current or aggregate state. We explore representing 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 \textit{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. We apply the classification to the specific task of identifying patients at risk of testing positive for hospital acquired colonization with \textit{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).