Deriving Trends in Historical and Real-Time Continuously Sampled Medical Data

A system which abstracts continuous data into trends is developed which consists of three consecutive processes: filtering which smooths the data; temporal interpolation which creates simple intervals between consecutive data points; and temporal inference which iteratively merges intervals which share similar characteristics into larger intervals.

Abstract

Monitors in Intensive Care Units generate large volumes of continuous data which can overwhelm a database and result in information overload for the medical staff. Instead of reasoning with individual data samples of one or more variables, it is better to work with the trend of the data i.e., whether the data is increasing, decreasing or steady. We have developed a system which abstracts continuous data into trends; it consists of three consecutive processes: filtering which smooths the data; temporal interpolation which creates simple intervals between consecutive data points; and temporal inference which iteratively merges intervals which share similar characteristics into larger intervals. Storing trends can result in a reduction in database volume. Our system has been applied both to historical and real-time data.