建立和评估基于投票算法的内部质量控制 (ViQC),基于患者的实时质量控制
Yuan Liu1, Hexiang Zheng2, Wanying Zhang1
1Department of Laboratory Medicine, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, Jiangsu, China.
概括
基于AI投票算法的内部质量控制 (ViQC) 系统通过提高错误检测效率来增强基于患者的实时质量控制 (PBRTQC). 这种先进的方法显著减少了在临床环境中识别错误所需的患者样本数量.
科学领域:
- 临床化学 临床化学
- 医疗保健中的人工智能
- 质量控制系统 质量控制系统
背景情况:
- 基于患者的实时质量控制 (PBRTQC) 是传统内部质量控制 (iQC) 的补充程序.
- 现有的PBRTQC系统在临床环境中面临实际挑战,需要提高精度和可靠性.
- 人工智能 (AI) 为提高医疗保健质量控制机制提供了潜在的解决方案.
研究的目的:
- 引入和开发基于人工智能的方法,以投票算法为基础的iQC (ViQC),用于PBRTQC.
- 使用人工智能提高现有的PBRTQC系统的精度和可靠性.
- 评估ViQC算法的分析性能和临床疗效.
主要方法:
- 从南京鼓楼医院对111,925名住院患者的血清葡萄糖检测结果进行了回顾性分析.
- 基于投票算法原则的投票iQC (ViQC) 算法的开发.
- 通过随机误差 (RE) 计算和与五种统计算法 (MA,EWMA,MM,MQ,MovSD) 的比较来评估分析性能.
主要成果:
- 结合各种机器学习模型的ViQC模型表现出极高的精度,假阳性率低于0.002.
- ViQC实现了高精度 (0.965),误差系数为2.0,所有评估的误差系数的AUC超过0.97.
- 与传统的PBRTQC方法相比,ViQC显著提高了错误检测效率,将所需的患者样本从724个减少到168个.
结论:
- 基于AI的PBRTQC系统 (ViQC) 在现实环境中表现出令人满意的性能.
- 与传统的PBRTQC统计方法相比,ViQC提供了优越的错误检测能力.
- 开发的ViQC算法提高了临床实验室测试质量控制的可靠性和效率.
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