  Clustering time-series data in healthcare is crucial for clinical phenotyping
to understand patients' disease progression patterns and to design treatment
guidelines tailored to homogeneous patient subgroups. While rich temporal
dynamics enable the discovery of potential clusters beyond static correlations,
two major challenges remain outstanding: i) discovery of predictive patterns
from many potential temporal correlations in the multi-variate time-series data
and ii) association of individual temporal patterns to the target label
distribution that best characterizes the underlying clinical progression. To
address such challenges, we develop a novel temporal clustering method,
T-Phenotype, to discover phenotypes of predictive temporal patterns from
labeled time-series data. We introduce an efficient representation learning
approach in frequency domain that can encode variable-length,
irregularly-sampled time-series into a unified representation space, which is
then applied to identify various temporal patterns that potentially contribute
to the target label using a new notion of path-based similarity. Throughout the
experiments on synthetic and real-world datasets, we show that T-Phenotype
achieves the best phenotype discovery performance over all the evaluated
baselines. We further demonstrate the utility of T-Phenotype by uncovering
clinically meaningful patient subgroups characterized by unique temporal
patterns.
