用专家堆叠提升脓毒症预测，实现早期干预与抗生素精准选择。
SepsisSuite: Beyond Risk Stratification -- A Comparative Analysis of Deep Fusion vs. Expert Stacking for Prescriptive Sepsis AI
- 分模块专家架构动态融合多模态数据，避免深层模型过拟合。
- 提前4小时预测脓毒症，AUC达0.915，误漏率降低48%。
- 支持临床决策，适合重症医学与AI医疗开发者使用。
脓毒症占全球重症监护室入院近20%，但传统预测模型常无法有效整合异构数据流，或因早期融合而性能脆弱。本文对比了端到端深度融合与上下文感知堆叠在脓毒症任务中的表现。我们最初设计的四模态分层门控注意力网络（SepsisFusionFormer）在MIMIC-IV数据集上因小抗生素队列（$N ≈ 2,100$）出现注意力饥饿，导致过拟合（AUC 0.66）。由此启发，我们提出更轻量的上下文感知混合专家（MoE）架构——SepsisLateFusion，将各模态视为独立专家（历史数据、时间序列、文本），由CatBoost元学习器动态加权。该方法在临床发病前4小时预测中达到SOTA的0.915 AUC；通过校准决策阈值，误漏病例减少48%，真正实现预防性干预。此外，在多类抗生素选择这一新预设任务中，四模态集成模型表现最佳（AUC 0.72）。所有模型已集成至可部署的Python框架SepsisSuite，开源地址：https://github.com/RyanCartularo/SepsisSuite-Info。
原文摘要 · Abstract (English)
Sepsis accounts for nearly 20% of global ICU admissions, yet conventional prediction models often fail to effectively integrate heterogeneous data streams, remaining either siloed by modality or reliant on brittle early fusion. In this work, we present a rigorous architectural comparison between End-to-End Deep Fusion and Context-Aware Stacking for sepsis tasks. We initially hypothesized that a novel Quad-Modal Hierarchical Gated Attention Network -- termed SepsisFusionFormer -- would resolve complex cross-modal interactions between vitals, text, and imaging. However, experiments on MIMIC-IV revealed that SepsisFusionFormer suffered from "attention starvation" in the small antibiotic cohort ($N \approx 2,100$), resulting in overfitting (AUC 0.66). This counterintuitive result informed the design of SepsisLateFusion, a "leaner" Context-Aware Mixture-of-Experts (MoE) architecture. By treating modalities as orthogonal experts -- the "Historian" (Static), the "Monitor" (Temporal), and the "Reader" (NLP) -- and dynamically gating them via a CatBoost meta-learner, we achieved State-of-the-Art (SOTA) performance: 0.915 AUC for prediction 4 hours prior to clinical onset. By calibrating the decision threshold for clinical safety, we reduced missed cases by 48% relative to the default operating point, thus opening a true preventative window for timely intervention over reactive alerts. Furthermore, for the novel prescriptive task of multi-class antibiotic selection, we demonstrate that a Quad-Modal Ensemble achieved the highest performance (0.72 AUC). These models are integrated into SepsisSuite, a deployment-ready Python framework for clinical decision support. SepsisSuite is available for free at: https://github.com/RyanCartularo/SepsisSuite-Info
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