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使用机器学习和ePath识别与长时间住院相关的临床途径中的关键差异真实世界数据:模型开发和验证研究

Saori Tou¹, Koutarou Matsumoto¹, Asato Hashinokuchi²

¹Department of Health Care Administration and Management, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.

JMIR medical informatics

|December 1, 2025

概括

机器学习确定了影响肺癌手术患者长期住院 (PLOS) 的关键临床差异. 该工具可以通过突出发烧和异常呼吸声等因素来改善临床决策和患者管理.

科学领域:

医疗信息学医疗信息学
临床数据科学临床数据科学
医疗保健中的机器学习

背景情况:

长时间住院 (PLOS) 会增加医疗保健效率低下和资源消耗.
识别导致PLOS的因素对于优化患者护理途径至关重要.

研究的目的:

开发和验证一种机器学习模型,以识别与PLOS相关的临床差异,用于接受视频辅助胸腔镜手术的肺癌患者.
通过使用来自ePath系统的真实世界数据,精确定位PLOS的关键预测因素.

主要方法:

分析了480名肺癌患者接受视频辅助胸腔镜手术的数据.
使用稀疏线性回归 (拉索,,弹性网) 和决策树组合 (随机森林,极端梯度增强) 的预测模型的开发.
使用导出和测试队列进行时间验证,通过AUC和Brier分数评估性能;用于因子识别的反事实分析.

主要成果:

斜坡回归模型显示了最佳的性能 (AUC 0.84/0.82,布赖尔得分 0.16/0.17).
与PLOS相关的关键变量包括异常的呼吸声,术后发烧,心律失常,行走障碍,排水移除并发症和肺空气泄漏.
临床差异被确定为PLOS的重要贡献者.

结论:

一个机器学习模型有效地识别了导致PLOS的临床途径中的关键差异.
这种自动化工具可以帮助临床决策,并提高肺癌手术的患者管理.

关键词:

在这里,我们可以看到AIAIAI.人工智能的人工智能是人工智能.临床途径临床途径在医院住院.机器学习是机器学习.视频辅助的胸腔镜外科手术

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