QuanTiMedAI：由智能体人工智能引导的量子增强时间序列模型用于心脏骤停死亡率预测
QuanTiMedAI: Quantum-Enhanced Time-Series Model guided by Agentic AI for Cardiac Arrest Mortality Prediction
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中文总结 AI 辅助
该研究针对心脏骤停死亡率预测的静态数据局限，提出QuanTiMedAI量子-智能体框架，结合智能体LLM与量子循环网络，在MIMIC-IV数据集上仅用605个参数实现0.852的AUROC，优于现有最先进基线。
中文摘要 AI 辅助
心脏骤停仍是重症监护病房中最致命的病症之一。尽管电子健康记录数据的可用性不断提升，但针对该人群的现有死亡率预测研究大多依赖于入院早期得出的静态汇总信息，此类方法忽略了患者在ICU住院期间生理恶化与恢复的时间演变过程。为解决这一局限，我们提出了QuanTiMedAI，这是一款为心脏骤停死亡率预测开发的量子-智能体框架，采用由智能体人工智能引导的量子增强时间序列模型。该系统结合了用于临床知情特征发现的智能体大语言模型（LLM）与用于感知时间的死亡率预测的紧凑型量子循环网络。研究结果表明，由智能体LLM引导的特征选择始终优于传统特征选择方法，且所提出的量子架构通过非线性特征增强实现了具有竞争力的预测性能，同时保持了极低的参数数量。通过对MIMIC-IV中心脏骤停患者队列的大量实验，QuanTiMedAI的量子增强架构仅用605个参数就达到了0.852的AUROC，比该任务当前的最先进基线提升了约2.9%。一项结构化 ablation研究系统验证了每个架构设计选择的贡献。这些结果表明，量子增强序列建模在使用显著更少参数的情况下，能够超越经典循环网络。
英文摘要
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's 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's 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.
发表机构
- North South University（北南大学）
- Memorial University（纪念大学）
机构由 AI 辅助整理，请以论文原文为准。
