INTELLI-PVA:基于信息样本注释的对比式主动学习,用于跨领域的患者-呼吸机异步检测
Lingwei Zhang1, Xue Feng1, Fei Lu1
1College of Information Engineering, Zhejiang University of Technology, Liuhe Rd. 288, Hangzhou 310023, China.
INTELLI-PVA框架允许使用人工智能准确,跨领域检测患者-呼吸机异步 (PVA). 这种人工智能解决方案克服了临床变化和数据挑战,改善了机械通风监测.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能在医学中的应用
- 关键护理医学 关键护理医学
背景情况:
- 患者-呼吸器异步 (PVA) 在机械呼吸的患者中很常见,并且会对结果产生负面影响.
- 实时PVA检测是困难的,因为患者与呼吸机相互作用的变化和PVA类型的重叠.
- 现有的人工智能系统在PVA检测中难以跨域概括.
研究的目的:
- 开发一个高效的人工智能 (AI) 框架,INTELLI-PVA,用于跨领域的患者-呼吸器异步 (PVA) 检测.
- 解决当前人工智能系统的局限性,包括PVA类型的临床变异性和形态重叠.
- 为了使人工智能辅助通风监测在各种临床环境中的实际部署.
主要方法:
- 开发了一种混合的两阶段PVA分类器,结合了深度学习模型和基于规则的算法.
- 利用对比式学习进行预训练和积极学习进行代域调整,最小的专家注释.
- 深度学习模型识别了复合PVA类型,而基于规则的算法根据触发签名区分了子类型.
主要成果:
- 在两个中心分类八种PVA类型时,INTELLI-PVA获得了0.849的F1平均得分.
- 该框架证明了呼吸治疗师级别的识别能力 (平均科恩的 κ=0.850) 在未见的数据上.
- 每个目标域只用1000个注释样本实现了有效的跨域检测.
结论:
- INTELLI-PVA为高精度,跨领域的PVA检测提供了实用和高效的解决方案.
- 该框架最大限度地减少了注释负担,在各种临床环境中促进了人工智能辅助的通风监测.
- 这种人工智能方法通过解决PVA挑战,提高了有效管理机械通风的能力.
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