using semi-parametric clustering applied to electronic health record time series data
We describe a exible framework for biomedical time series clustering that aims to facilitate the use of temporal information derived from EHRs in a meaningful way. As a case study, we use a dataset indicating the presence of physician ordered glucose tests for a population of hospitalized patients and aim to group individuals with similar disease status. Our approach pairs Hidden Markov Models (HMMs) to abstract variable length temporal information, with non-parametric spectral clustering to reveal inherent group structure. We focus on systematically comparing the performance of our approach with two alternative clustering methods that use various time series statistics instead of HMM based temporal features. Intrinsic evaluation of cluster quality shows a dramatic improvement using the HMM based feature set, generating clusters that indicate more than 90\% of patients are similar to members of their own cluster, and distinct from patients in neighboring clusters.
semi-parametric clustering electronic health records time series data