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使用统计或机器学习方法开发的临床预测模型的稳定性.
Richard D Riley1, Gary S Collins2
1Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK.
Biometrical journal. Biometrische Zeitschrift
|July 19, 2023
概括
在小数据集上开发的临床预测模型可能不稳定,导致不准确的风险预测. 研究人员应使用提出的方法评估模型不稳定性,以确保可靠的健康结果估计.
科学领域:
- 生物统计学 生物统计学
- 医疗信息学 医疗信息学
- 临床流行病学临床流行病学
背景情况:
- 临床预测模型对于估计个体健康风险至关重要.
- 模型开发受数据集大小,预测因素和分析方法的影响.
- 小数据集可能导致不稳定的模型和不可靠的风险预测.
研究的目的:
- 在临床预测模型中定义和评估模型稳定性.
- 为了证明模型不稳定性如何影响预测准确性和校准.
- 提出在开发过程中评估模型不稳定的方法.
主要方法:
- 在估计风险中定义了四个级别的模型稳定性.
- 利用了涉及统计和机器学习模型的模拟和案例研究.
- 采用引导重抽样,生成多个模型来进行不稳定性评估.
- 拟议的不稳定性图表和措施,包括平均绝对预测误差.
主要成果:
- 估计风险中的模型不稳定性往往是相当大的,特别是小数据集.
- 不稳定性表现为对新数据应用时预测的错误校准.
- 拟议的不稳定性评估可以揭示模型预测中的潜在不可靠性.
结论:
- 研究人员必须在开发阶段评估模型的不稳定性.
- 不稳定性情节和措施有助于批判性评估,公平性评估和验证计划.
- 确保模型稳定性对于可靠的临床风险预测至关重要.
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