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.

翻译：脓毒症是一种异质性综合征。识别具有临床差异的表型有助于实现更精准的治疗策略。近年来，许多研究者将聚类算法应用于脓毒症患者。然而，聚类过程很少纳入临床相关性，这可能限制了其反映临床差异表型的能力。我们提出了NPCNet，这是一种具有目标导航器的新型深度聚类网络，它整合了时序电子健康记录（EHRs），以更好地将脓毒症表型与临床意义对齐。我们识别出四种具有不同SOFA评分轨迹的脓毒症表型（$α$、$β$、$γ$和$δ$）。值得注意的是，虽然$α$和$δ$表型在早期均表现出严重状况，但NPCNet能有效区分可能好转的患者（$α$）与存在恶化风险的患者（$δ$）。此外，通过治疗效果分析，我们发现$α$、$β$和$δ$表型可能受益于早期血管加压药物的使用。结果表明，NPCNet通过揭示具有临床差异的表型，增强了精准治疗策略。