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Efficient unsupervised algorithms for the detection of patterns in time series data, often called motifs, have been used in many applications, such as identifying words in different languages, detecting anomalies in ECG readings, and finding similarities between images. This paper present a process that creates a personalized multivariate time series representation - a Multivariate Time Series Amalgam (MTSA) - of physiological data and laboratory results that physicians can visually interpret.
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