在临床实践中,圆形数字和利值的隐藏风险
Benjamin J Lengerich1,2,3, Rich Caruana4, Mark E Nunnally5
1Massachusetts Institute of Technology, Cambridge, MA, USA. lengerich@wisc.edu.
临床决策中的圆数值扭曲了患者的风险评估,导致了危险的异常. 本研究引入了一种机器学习模型,用于识别和解决这些问题,以改善医疗保健结果.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 临床风险评估临床风险评估
背景情况:
- 临床决策经常使用从连续数据中得出的简化,离散的风险水平.
- 虽然常见,但圆形数值值可能会对风险评估的准确性造成重大扭曲.
研究的目的:
- 开发一种可解释的机器学习模型,以检测基于值的临床实践引起的异常.
- 系统地发现风险评估中因使用圆形数值值而产生的扭曲.
主要方法:
- 开发一个可解释的机器学习模型.
- 使用模拟,现实世界患者数据和纵向研究进行验证.
- 对死亡风险不连续性和反因果悖论的分析.
主要成果:
- 证明圆形数值值在死亡风险评估中会造成不连续性和悖论.
- 鉴定了风险评估中的持续异常,尽管医学进步.
- 强调了基于门的实践对患者结果的影响.
结论:
- 临床实践中的圆形数值值导致了风险评估中的重大和持久的扭曲.
- 在医疗保健中,急需使用动态和细微的风险评估方法.
- 重新评估临床方案,特别是在重症监护中,对于与风险的持续性质保持一致并改善患者的治疗结果至关重要.
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