A novel method for the efficient retrieval of similar multiparameter physiologic time series using wavelet-based symbolic representations.

An important challenge in data mining is in identifying "similar" temporal patterns that may illuminate hidden information in a database of time series. We are actively engaged in the development of a temporal database of several thousand ICU patient records that contains time-varying physiologic measurements recorded over each patient's ICU stay. The discovery of multiparameter temporal patterns that are predictive of physiologic instability may aid clinicians in optimizing care for critically-ill patients. In this paper, we introduce a novel temporal similarity metric based on a transformation of time series data into an intuitive symbolic representation. The symbolic transform is based on a wavelet decomposition to characterize time series dynamics at multiple time scales. The symbolic transformation allows us to utilize classical information retrieval algorithms based on a vector-space model. Our algorithm is capable of assessing the similarity between multi-dimensional time series and is computationally efficient. We utilized our algorithm to identify similar physiologic patterns in hemodynamic time series from ICU patients. The results of this study demonstrate that similarities between different patient time series may have meaningful physiologic interpretations in the detection of impending hemodynamic deterioration. Thus, our framework may be of potential use in clinical decision-support systems. As a generalized time series similarity metric, the algorithms that are described have applications in several other domains as well.