心脏骤停仍然是重症监护室遇到的最致命的情况之一。尽管电子健康记录数据越来越多,，现有的死亡率预测研究在很大程度上依赖于早期入院得出的静态总结。这些方法忽略了患者 27 秒入住 ICU 期间生理恶化和恢复的时间进展。为了解决这一限制,，我们引入了 QuanTiMedAI,，这是一种量子代理框架，该框架使用代理 AI 引导的量子增强时间序列模型来预测心脏骤停死亡率。所提出的系统将用于临床知情特征发现的代理大语言模型 (LLM) 与用于时间感知死亡率预测的紧凑量子循环网络相结合。我们的研究结果表明，代理 LLM 引导的特征选择始终优于传统的特征选择方法,，并且所提出的量子架构通过非线性特征增强实现了有竞争力的预测性能，同时保持参数数量非常低。通过对心脏骤停患者的 MIMIC-IV 队列进行广泛实验, QuanTiMedAI的 量子增强架构仅使用 605 个参数, 就获得了 0.852 的 AUROC，与当前这项任务的最先进基线相比，提高了约 2.9\%。结构化消融研究系统地验证了每个建筑设计选择的贡献。这些结果表明，量子增强序列建模可以超越经典的循环网络，同时使用更少的参数。

Cardiac arrest remains one of the most lethal conditions encountered in intensive care units. Despite the growing availability of electronic health record data, existing mortality prediction studies in this population largely depend on static summaries derived from early admission. Such approaches ignore the temporal progression of physiological deterioration and recovery that unfolds throughout a patient的 ICU stay. To address this limitation, we introduce QuanTiMedAI, a quantum-agentic framework developed for cardiac arrest mortality prediction using agentic AI guided quantum enhancement time series model. The proposed system combines an agentic large language model (LLM) for clinically informed feature discovery with a compact quantum recurrent network for temporality aware mortality prediction. Our findings demonstrate that agentic LLM-guided feature selection consistently outperforms conventional feature selection approaches, and the proposed quantum architecture achieves competitive predictive performance through nonlinear feature enhancement while keeping the number of parameters very low. Through extensive experimentation on a MIMIC-IV cohort of cardiac arrest patients, QuanTiMedAI的 quantum-enhanced architecture attains an AUROC of 0.852 using only 605 parameters, an improvement of approximately 2.9\% over a current state-of-the-art baseline for this task. A structured ablation study systematically validates the contribution of each architectural design choice. These results show that quantum-enhanced sequential modeling can exceed classical recurrent networks while using substantially fewer parameters.

主题: 人工智能 (cs.AI); 新兴技术 (cs.ET)

Subjects: Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET)