Predicting short-term ICU outcomes using a sequential contrast motif based classification framework

Critical ICU events like acute hypotension and septic shock are dangerous complications, leading to multiple organ failures and eventual death. Previously, pattern mining algorithms have been employed for extracting interesting rules in various clinical domains. However, the extracted rules are directly investigated by clinicians for diagnosing a disease. Towards this purpose, there is a need to develop advanced prediction models which integrate dynamic patterns to learn a patient's physiological condition. In this study, a sequential contrast patterns-based classification framework is presented for detecting critical patient events, like hypotension and septic shock. Initially, a set of sequential patterns are obtained by using a contrast mining algorithm. Later, these patterns undergo post-processing, for conversion to two novel representations-(1) frequency-based feature space and (2) ordered sequences of patterns, which conserve positional information of a pattern in a time series sequence. Each of these representations are automatically used for developing classification models using SVM and HMM methods. Our results on hypotension and septic shock datasets from a large scale ICU database demonstrate better predictive capabilities, when sequential patterns are used as features.