机械通风试验研究的虚拟患者与时间演变:一个随机模型方法
Christopher Yew Shuen Ang1, Yeong Shiong Chiew1, Xin Wang1
1School of Engineering, Monash University Malaysia, Selangor, Malaysia.
Computer methods and programs in biomedicine
|August 2, 2023
概括
这项研究使用随机模拟开发了虚拟患者 (VP) 模型,以创建现实的呼吸衰竭概况. 这些VP使得体检测成为可能,减少了对机械通风进行昂贵的临床试验的需求.
科学领域:
- 计算生物学是一种计算生物学.
- 医疗信息学医学信息学
- 生物医学工程 生物医学工程
背景情况:
- 医疗保健数据集经常受到数据稀缺和阶级不平衡的影响,阻碍了研究和开发.
- 临床验证的虚拟患者 (VP) 模型为重症监护机构的合成数据生成提供了解决方案.
- 这项研究的重点是为机械通风呼吸系统衰竭创建一个时间变化的VP配置文件.
研究的目的:
- 使用随机模型合成机械通风呼吸衰竭的现实,时间变化的虚拟患者 (VP) 档案.
- 为了证明使用这些VP用于in-silico模拟和验证的可行性.
- 减少对传统临床试验的依赖,以开发机械通风协议.
主要方法:
- 使用临床队列的呼吸弹性 (Ers) 数据开发了一个随机模型,平均超过30分钟的间隔.
- 生成的未来 Ers 数据与正常分布的随机噪声用于 VP 合成.
- 通过蒙特卡洛模拟和追溯Ers配置文件匹配验证VP,将合成队列与独立患者数据进行比较.
主要成果:
- 为压力控制 (PC) 和体积控制 (VC) 通风模式生成了 120,000 个三小时的 VPs.
- 优化了随机模拟,5-10%的噪声和20万次代,以实现现实的 Ers 配置文件.
- 在自我验证方面取得了高准确性 (PC的MSE<0.099%,VC的<0.051%) 并通过虚拟试验证明了队列级验证.
结论:
- 暂时不断发展的VP可用于设计,开发和优化床边机械通风 (MV) 指导协议.
- 基于随机模拟的VP减少了漫长而昂贵的临床试验的需要.
- 这些VP促进了统计学上强大的虚拟试验,最终改善了机械通风中的患者护理.
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