Risk prediction for acute hypotensive patients by using gap constrained sequential contrast patterns

The development of acute hypotension in a critical care patient causes decreased tissue perfusion, which can lead to multiple organ failures. Existing systems that employ population level prognostic scores to stratify the risks of critical care patients based on hypotensive episodes are suboptimal in predicting impending critical conditions, or in directing an effective goal-oriented therapy. In this work, we propose a sequential pattern mining approach which target novel and informative sequential contrast patterns for the detection of hypotension episodes. Our results demonstrate the competitiveness of the approach, in terms of both prediction performance as well as knowledge interpretability. Hence, sequential patterns-based computational biomarkers can help comprehend unusual episodes in critical care patients ahead of time for early warning systems. Sequential patterns can thus aid in the development of a powerful critical care knowledge discovery framework for facilitating novel patient treatment plans.