对个体患者治疗决定的信心:顺序评估策略
Nina Orwitz1, Thaddeus Tarpey1, Eva Petkova1
1180 Madison Avenue, New York, NY 10016, United States of America.
概括
这项研究引入了新的方法来测量患者数据积累的治疗决策规则 (TDR) 的信心. 这些措施有助于量化不确定性,确保在不断变化的医疗数据下提供最佳的患者治疗.
科学领域:
- 医疗信息学 医疗信息学
- 临床决策支持 临床决策支持
- 生物统计学 生物统计学
背景情况:
- 医疗技术产生复杂的数据,需要先进的治疗决策规则 (TDR).
- 现有的TDR面临的不确定性关于个体患者的最佳性,尽管在人口层面的表现.
- 连续程序中的实时数据积累对TDR应用提出了挑战.
研究的目的:
- 开发和量化使用实时,积累患者数据的TDRs的信任度.
- 评估与个人患者的TDR建议相关的不确定性.
- 为更新治疗决策和信心提供一个框架,随着更多的数据变得可用.
主要方法:
- 通过患者共变量与治疗决策边界的距离进行拟议的信心估计.
- 引入了使用最终治疗分配的有条件概率的信心测量.
- 模拟数据积累和对信心的重新评估,直到达到高水平.
主要成果:
- 证明,与决策边界的距离更大与对TDR的信心更高有关.
- 展示了条件概率对于实时信任评估的有用性.
- 模拟研究证实了拟议方法在量化TDR不确定性的有效性.
结论:
- 拟议的信任度有效量化TDR中的不确定性,使用顺序数据.
- 这些方法可以通过提供实时的信心评估来增强临床决策.
- 为在临床试验和实践中实施这些信心措施提供了实际建议.
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