Semantic Reference Model in Medical Time Series

Abstract

The analysis of time series databases is very important in the area of medicine. Most of the approaches that address this problem are based on numerical algorithms that calculate distances, clusters, index trees, etc. However, a domain-dependent analysis sometimes needs to be conducted to search for the symbolic rather than numerical characteristics of the time series. This paper focuses on our work on the discovery of reference models in time series of isokinetics data and a technique that transforms the numerical time series into symbolic series. We briefly describe the algorithm used to create reference models for population groups and its application in the real world. Then, we describe a method based on extracting semantic information from a numerical series. This symbolic information helps users to efficiently analyze and compare time series in the same or similar way as a domain expert would.