医疗保健5.0驱动的临床智能:学习-预测-监测-检测-正确的框架,用于系统地整合人工智能到重症监护中
Hanene Boussi Rahmouni1,2, Nesrine Ben El Hadj Hassine1,3, Mariem Chouchen1
1Research Laboratory of Biophysics and Medical Technologies, The Higher Institute of Medical Technologies of Tunis, University of Tunis el Manar, Tunis 1006, Tunisia.
Healthcare (Basel, Switzerland)
|October 29, 2025
概括
学习-预测-监测-检测-正确 (LPMDC) 框架将人工智能整合到重症监护中,提高患者的治疗结果并减少临床医生的工作负担. 这种医疗保健5.0方法确保了在人类监督下安全的AI实施.
科学领域:
- 关键护理医学 关键护理医学
- 医疗保健中的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 医疗保健5.0强调智能,以人为中心的护理系统.
- 重症监护病房 (ICU) 产生大量数据,需要实时决策.
- 目前的系统缺乏在重症监护中安全整合人工智能的框架.
研究的目的:
- 开发和验证学习-预测-监测-检测-正确 (LPMDC) 框架,以系统地整合关键护理中的AI.
- 加强预测分析,患者监测,警报和决策支持,同时保持临床监督.
主要方法:
- 整合医疗保健5.0原则的理论建模.
- 文献综合 (2020-2024年),ICU工作流分析 (15个国际站点) 和技术评估.
- 由24名重症监护医生和信息专家进行多轮专家验证.
主要成果:
- 观察到的显著改善:死亡率降低30%,ICU停留时间减少18%,认知负载减少45%,败血症捆绑合规增加85%.
- 人工智能在早期败血症预测 (3小时前) 中实现了80%的灵敏度,错误阳性<15%.
- 在预测呼吸衰竭,预防心血管危机和自动化呼吸机管理方面表现出有效性;数字双胞胎和IoMT集成.
结论:
- 在LPMDC框架提供了一个全面的基础,系统的AI整合在重症监护,优先考虑安全和人类监督.
- 循环五相架构支持通过反循环和优化超越人类认知极限的处理.
- 临床验证显示,患者的治疗结果,运营效率和临床医生的满意度都有可衡量的改善.
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