NPCNet: Navigator-Driven Pseudo Text for Deep Clustering of Early Sepsis Phenotyping
This work addresses the need for more precise treatment strategies in sepsis by uncovering clinically relevant phenotypes, though it appears incremental as it builds on existing clustering methods with added clinical integration.
The paper tackled the problem of identifying clinically distinct sepsis phenotypes from temporal Electronic Health Records using NPCNet, a deep clustering network with a target navigator, resulting in the identification of four phenotypes (α, β, γ, δ) with divergent SOFA trajectories and improved differentiation between patients likely to improve versus deteriorate.
Sepsis is a heterogeneous syndrome. Identifying clinically distinct phenotypes may enable more precise treatment strategies. In recent years, many researchers have applied clustering algorithms to sepsis patients. However, the clustering process rarely incorporates clinical relevance, potentially limiting to reflect clinically distinct phenotypes. We propose NPCNet, a novel deep clustering network with a target navigator that integrates temporal Electronic Health Records (EHRs) to better align sepsis phenotypes with clinical significance. We identify four sepsis phenotypes ($α$, $β$, $γ$, and $δ$) with divergence in SOFA trajectories. Notably, while $α$ and $δ$ phenotypes both show severe conditions in the early stage, NPCNet effectively differentiates patients who are likely to improve ($α$) from those at risk of deterioration ($δ$). Furthermore, through the treatment effect analysis, we discover that $α$, $β$, and $δ$ phenotypes may benefit from early vasopressor administration. The results show that NPCNet enhances precision treatment strategies by uncovering clinically distinct phenotypes.