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生命体征测量表明终端数字偏差和边界效应
Oliver Kleinig1,2, Minh-Son To3, Christopher D Ovenden1,2
1Department of Neurology, Department of Neurosurgery, Royal Adelaide Hospital, Adelaide, South Australia, Australia.
生命体征数据,包括心率和血压,显示偏差,如边界效应和偏好圆形数字. 这些数值偏差可以影响患者护理和机器学习模型.
科学领域:
- 医疗信息学 医疗信息学
- 临床数据分析 临床数据分析
- 生物统计学 生物统计学
背景情况:
- 生命体征对于患者监测和临床决策至关重要.
- 已知生命体征记录的潜在偏差,例如偏好圆形数字.
- 对生命体征的边界效应数据以前没有在医学上进行过研究.
研究的目的:
- 调查记录的生命体征中的边界效应和数值偏差的存在.
- 分析生命体征数据中特定数值的潜在过度报告.
- 了解这些偏见如何影响临床护理升级和机器学习模型开发.
主要方法:
- 对生命体征数据 (心率,呼吸率,氧和率,缩血压) 的分析,来自三级医院两年来的电子病历.
- 使用多项式回归来检测超出范围的值的不足报告和以0,2和5结尾的数字的过度报告.
- 在收集的数据中评估了边界效应和数值偏好.
主要成果:
- 在心率,氧和和静脉血压方面观察到边界效应,在正常范围内的值被不成比例地记录.
- 在缩心率,呼吸率和血压中检测到偶数偏差.
- 在心率和血压方面,人们注意到对5的倍数的偏好,在心率数据中,10的倍数的独立过度代表.
结论:
- 生命体征数据,尽管看起来客观,但容易受到各种记录偏差的影响.
- 这些已识别的偏见可能会影响患者护理途径和治疗决策.
- 这些发现对开发和训练依赖生命体征数据的机器学习算法具有重大意义.
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