Recognizing diseases from physiological time series data

Abstract

As the level of hospital informatization raises, it is possible to obtain huge amount of physiological data from bedside monitor and other medical instruments. The goal for this work is to recognize diseases from physiological data by unique combinations of representative patterns for different diseases. The representative patterns are clustered from the original physiological time series data, e.g. pulse, respiration rate, blood pressure, heart rate and oxygen saturation rate. Within a disease, to compose the set of representative patterns into a interrelated structure, we bring in Allen’s interval relations to describe the temporal relations between each of two neighboring patterns. We use Chinese Restaurant Process (CRP) to draw the uncertainty of every temporal relations that links two representative patterns. The two algorithms are combined into the model we use in this work, called probabilistic model. The experimental results suggests our model has potential in recognizing diseases.