Sepsis poses a major global health threat, accounting for millions of deaths annually and significant economic costs. Accurately predicting the risk of mortality in sepsis patients enables early identification, promotes the efficient allocation of medical resources, and facilitates timely interventions, thereby improving patient outcomes. Current methods typically utilize only one type of data--either constant, temporal, or ICD codes. This study introduces a novel approach, the Time-Constant Kolmogorov-Arnold Network (TCKAN), which uniquely integrates temporal data, constant data, and ICD codes within a single predictive model. Unlike existing methods that typically rely on one type of data, TCKAN leverages a multi-modal data integration strategy, resulting in superior predictive accuracy and robustness in identifying high-risk sepsis patients. Validated against the MIMIC-III and MIMIC-IV datasets, TCKAN surpasses existing machine learning and deep learning methods in accuracy, sensitivity, and specificity. Notably, TCKAN achieved AUCs of 87.76% and 88.07%, demonstrating superior capability in identifying high-risk patients. Additionally, TCKAN effectively combats the prevalent issue of data imbalance in clinical settings, improving the detection of patients at elevated risk of mortality and facilitating timely interventions. These results confirm the model's effectiveness and its potential to transform patient management and treatment optimization in clinical practice. Although the TCKAN model has already incorporated temporal, constant, and ICD code data, future research could include more diverse medical data types, such as imaging and laboratory test results, to achieve a more comprehensive data integration and further improve predictive accuracy.

翻译：脓毒症构成全球重大健康威胁，每年导致数百万人死亡并造成显著经济负担。准确预测脓毒症患者的死亡风险有助于早期识别高危患者、促进医疗资源高效配置、推动及时干预，从而改善患者预后。现有方法通常仅利用单一数据类型——或为静态数据，或为时序数据，或为ICD编码。本研究提出一种创新方法，即时不变柯尔莫哥洛夫-阿诺德网络（TCKAN），该模型首次将时序数据、静态数据与ICD编码整合于统一预测框架中。与现有仅依赖单一数据类型的方案不同，TCKAN采用多模态数据融合策略，在识别高危脓毒症患者方面展现出更优的预测精度与鲁棒性。基于MIMIC-III和MIMIC-IV数据集的验证表明，TCKAN在准确率、灵敏度和特异度上均超越现有机器学习与深度学习方法。值得注意的是，TCKAN分别取得87.76%和88.07%的AUC值，彰显了其识别高危患者的卓越能力。此外，TCKAN有效应对临床环境中普遍存在的数据不平衡问题，提升了对高死亡风险患者的检测效能，为及时干预创造了条件。这些结果证实了模型的有效性及其在临床实践中优化患者管理与治疗决策的转化潜力。尽管TCKAN模型已整合时序、静态与ICD编码数据，未来研究可纳入更丰富的医疗数据类型（如影像学检查与实验室检测结果），以实现更全面的数据整合并进一步提升预测精度。