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一种贝叶斯式方法,用于预测化学疗法患者的不确定性,这些患者有急性护理利用风险
Claudio Fanconi1, Anne de Hond2, Dylan Peterson3
1Department of Information Technology and Electrical Engineering, ETH Zürich, Zürich, Switzerland; Department of Medicine (Biomedical Informatics), Stanford University, Stanford, USA.
EBioMedicine
|June 3, 2023
概括
贝叶斯逻辑拉索回归 (BLLR) 模型提供与癌症患者风险标准拉索类似的预测性能,但提供关键的不确定性估计. 这些模型通过识别高风险患者子组来增强临床决策.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 临床决策支持 临床决策支持
背景情况:
- 机器学习 (ML) 越来越多地用于医学,后勤回归 (LASSO) 提供患者风险的点估计.
- 贝叶斯逻辑LASSO回归 (BLLR) 提供预测分布和不确定性估计,但在临床实践中未得到充分利用.
研究的目的:
- 评估各种BLLR模型的预测性能与标准LASSO回归相比.
- 评估BLLR在理解癌症患者风险分层中的预测不确定性的有用性.
主要方法:
- 利用癌症患者开始化疗的高维电子健康记录 (EHR) 数据.
- 将多个BLLR模型与一个LASSO模型进行比较,使用80-20随机分割和10倍交叉验证.
- 预测了化疗开始后急性护理利用率 (ACU) 的风险.
主要成果:
- LASSO模型实现了0.806的AUROC;BLLR与Horseshoe+之前显示了可比性能 (AUROC:0.807).
- BLLR为个人预测提供了基本的不确定性估计,并确定了不确定的预测.
- 预测不确定性在患者子组之间有显著差异,包括种族,癌症类型和阶段.
结论:
- BLLR模型是一个有希望的,未得到充分利用的工具,其性能与 LASSO 相同,可解释性更强.
- 通过突出需要更密切监测的患者子组,BLLR量化不确定性的能力有助于临床决策.
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