基于不同的机器学习方法,在住院患者中构建和验证肺栓塞风险预测模型
Tao Huang1, Zhihai Huang1, Xiaodong Peng1
1Emergency Medicine Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China.
Frontiers in cardiovascular medicine
|July 10, 2024
概括
这项研究开发了机器学习模型,用于预测住院患者的肺栓塞 (PE). 随机森林模型证明了PE风险预测的最高准确性和临床益处.
科学领域:
- 医疗信息学 医疗信息学
- 临床预测建模临床预测建模
- 医疗保健服务研究 医疗服务研究
背景情况:
- 肺栓塞 (PE) 对住院患者构成重大风险.
- 准确的风险预测对于及时干预和改善患者结果至关重要.
- 现有的预测方法可能需要通过先进的分析技术进行增强.
研究的目的:
- 开发和比较各种机器学习 (ML) 模型,用于预测住院患者的肺栓塞 (PE).
- 评估不同ML算法的预测性能和临床实用性.
- 确定在临床环境中对PE风险评估最有效的ML模型.
主要方法:
- 对332名住院患者 (172名PE阳性,160名PE阴性) 进行了回顾性研究.
- 开发了六种ML模型:物流回归,决策树,随机森林,天真贝叶斯,支持向量机和AdaBoost.
- 模型性能使用接收器操作特征 (ROC) 曲线分析 (曲线下的面积 - AUC) 和决策曲线分析 (DCA) 来评估.
主要成果:
- 后勤回归确定了深静脉血栓形成,D-二次体升高,激活部分前激素时间缩短,红细胞分布宽度增加作为PE的独立风险因素.
- 随机森林 (RF) 模型实现了最高的曲线下的面积 (AUC) 0.778.8.
- 决策曲线分析 (DCA) 证实随机森林模型提供了最大的临床益处.
结论:
- 机器学习模型,特别是Random Forest,显示出预测住院患者肺栓塞的重大前景.
- 与其他评估的ML模型相比,随机森林模型显示出更高的预测效率和临床实用性.
- 这项研究突出了ML在提高PE在临床实践中的识别和管理方面的潜力.
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