Multivariate sequential contrast pattern mining and prediction models for critical care clinical informatics

Data mining and knowledge discovery involves efficient search and discovery of patterns in data that are able to describe the underlying complex structure and properties of the corresponding system. To be of practical use, the discovered patterns need to be novel, informative and interpretable. Large-scale unstructured biomedical databases such as electronic health records (EHRs) tend to exacerbate the problem of discovering interesting and useful patterns. Typically, patients in intensive care units (ICUs) require constant monitoring of vital signs. To this purpose, signicant quantities of patient data, coupled with waveform signals are gathered from biosensors and clinical information systems. Subsequently, clinicians face an enormous challenge in the assimilation and interpretation of large volumes of unstructured, multidimensional, noisy and dynamically fluctuating patient data. The availability of de-identified ICU datasets like the MIMIC-II (Multiparameter Intelligent Monitoring in Intensive Care) databases provide an opportunity to advance medical care, by benchmarking algorithms that capture subtle patterns associated with specific medical conditions. Such patterns are able to provide fresh insights into disease dynamics over long time scales. In this research, we focus on the extraction of computational physiological markers, in the form of relevant medical episodes, event sequences and distinguishing sequential patterns. These interesting patterns known as sequential contrast patterns are combined with patient clinical features to develop powerful clinical prediction models. Later, the clinical models are