Clinical outcome-guided deep temporal clustering for disease progression subtyping.

objectiveComplex diseases exhibit heterogeneous progression patterns, necessitating effective capture and clustering of longitudinal changes to identify disease subtypes for personalized treatments. However, existing studies often fail to design clustering-specific representations or neglect clinical outcomes, thereby limiting the interpretability and clinical utility.

methodWe design a unified framework for subtyping longitudinal progressive diseases. We focus on effectively integrating all data from disease progressions and improving patient representation for downstream clustering. Specifically, we propose a clinical Outcome-Guided Deep Temporal Clustering (OG-DTC) that generates representations informed by clustering and clinical outcomes. A GRU-based seq2seq architecture captures the temporal dynamics, and the model integrates k-means clustering and outcome regression to facilitate the formation of clustering structures and the integration of clinical outcomes. The learned representations are clustered using a Gaussian mixture model to identify distinct subtypes. The clustering results are extensively validated through reproducibility, stability, and significance tests.

resultsWe demonstrated the efficacy of our framework by applying it to three Alzheimer's Disease (AD) clinical trials. Through the AD case study, we identified three distinct subtypes with unique patterns associated with differentiated clinical declines across multiple measures. The ablation study revealed the contributions of each component in the model and showed that jointly optimizing the full model improved patient representations for clustering. Extensive validations showed that the derived clustering is reproducible, stable, and significant.

conclusionOur temporal clustering framework can derive robust clustering applicable for subtyping longitudinal progressive diseases and has the potential to account for subtype variability in clinical outcomes.