Department of Emergency, The First Hospital of Hebei Medical University, Shijiazhuang, China.
System Integration Center, China Mobile Communication Group Hebei Co., LTD., Shijiazhuang, China.
J Transl Med. 2024 Nov 4;22(1):996. doi: 10.1186/s12967-024-05792-6.
Cardiac arrest presents a variety of causes and complexities, making it challenging to develop targeted treatment plans. Often, the original data are either inadequate or lack essential patient information. In this study, we introduce an intelligent system for diagnosing and treating in-hospital cardiac arrest (IHCA), aimed at improving the success rate of cardiopulmonary resuscitation and restoring spontaneous circulation.
To compensate for insufficient or incomplete data, a hybrid mega trend diffusion method was used to generate virtual samples, enhancing system performance. The core of the system is a modified episodic deep reinforcement learning module, which facilitates the diagnosis and treatment process while improving sample efficiency. Uncertainty analysis was performed using Monte Carlo simulations, and dependencies between different parameters were assessed using regular vine copula. The system's effectiveness was evaluated using ten years of data from Utstein-style IHCA registries across seven hospitals in China's Hebei Province.
The system demonstrated improved performance compared to other models, particularly in scenarios with inadequate data or missing patient information. The average reward scores in two key stages increased by 2.3-9 and 9.9-23, respectively.
The intelligent diagnosis and treatment effectively addresses IHCA, providing reliable diagnosis and treatment plans in IHCA scenarios. Moreover, it can effectively induce cardiopulmonary resuscitation and restoration of spontaneous circulation processes even when original data are insufficient or basic patient information is missing.
心脏骤停的病因和复杂性多种多样，因此难以制定针对性的治疗计划。通常，原始数据要么不充分，要么缺乏必要的患者信息。在本研究中，我们引入了一种用于诊断和治疗院内心脏骤停（IHCA）的智能系统，旨在提高心肺复苏的成功率并恢复自主循环。
为了弥补数据不足或不完整的问题，我们使用混合 mega 趋势扩散方法生成虚拟样本，从而提高系统性能。该系统的核心是一个经过修改的情节式深度强化学习模块，它可以促进诊断和治疗过程，同时提高样本效率。使用蒙特卡罗模拟进行不确定性分析，并使用正则 vine Copula 评估不同参数之间的依赖性。我们使用来自中国河北省 7 家医院的 10 年 Utstein 风格 IHCA 注册数据评估了该系统的有效性。
与其他模型相比，该系统的性能得到了提高，特别是在数据不足或缺少患者信息的情况下。两个关键阶段的平均奖励得分分别提高了 2.3-9 和 9.9-23。
该智能诊断和治疗系统有效地解决了 IHCA 问题，为 IHCA 场景提供了可靠的诊断和治疗计划。此外，即使原始数据不足或基本患者信息缺失，它也可以有效地诱导心肺复苏和自主循环恢复过程。
