Classification of patients using novel multivariate time series representations of physiological data

I will present two novel multivariate time series representations to classify physiological data of different lengths.The representations may be applied to any group of multivariate time series data that examine the state or health of an entity. Multivariate Bag-of-Patterns and Stacked Bags-of-Patterns improve on their univariate counterpart, inspired by the bag-of-words model, by using multiple time series and analyzing the data in a multivariate fashion. My collaborators and I also borrow techniques from the natural language processing domain such as term frequency and inverse document frequency to improve classification accuracy. I will introduce a technique named inverse frequency and present experimental results on classifying patients who have experienced acute episodes of hypotension.