T-Phenotype: Discovering Phenotypes of Predictive Temporal Patterns in Disease Progression

TL;DR

T-Phenotype is a novel method for discovering predictive temporal patterns in multivariate healthcare time-series data, enabling better patient phenotyping and understanding disease progression.

Contribution

It introduces a frequency domain representation learning approach and a path-based similarity measure for effective temporal clustering in healthcare data.

Findings

Achieves superior phenotype discovery performance over baselines.

Uncovers clinically meaningful patient subgroups.

Effective in synthetic and real-world datasets.

Abstract

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.