基于机器学习的血液透析患者的日内分析性低血压预测:一个多中心的回顾性研究
Jingjing Dong1, Kang Wang2, Jingquan He3
1Clinical Medical Research Center, the Second Clinical Medical College of Jinan University, Shenzhen People's Hospital, Jinan University, Shenzhen 518020, China; Institute of Nephrology and Blood Purification, the First Affiliated Hospital of Jinan University, Jinan University, Guangzhou 510630, China.
Computer methods and programs in biomedicine
|July 10, 2023
概括
机器学习有效地预测血液透析患者的日内分析性低血压 (IDH). 轻GBM模型为风险查和及时干预提供了可靠的工具,改善了患者的治疗结果.
科学领域:
- 科和人工智能的人工智能
- 临床信息学 临床信息学
- 在医疗保健中的预测建模.
背景情况:
- 内透析性低血压 (IDH) 是血液透析 (HD) 患者的重大并发症,与不良临床结果有关.
- 有效的风险预测模型对于在患有IDH的HD患者的查和临床决策至关重要.
研究的目的:
- 开发和评估机器学习 (ML) 模型,用于预测血液透析 (HD) 患者内透析性低血压 (IDH) 的风险.
- 识别导致IDH风险的关键变量,以改善患者管理.
主要方法:
- 利用了62,227个透析疗程的大数据集,随机分为训练,测试和验证集.
- 开发了两种ML模型:用于预测下一次治疗的IDH-A (27个变量) 和用于治疗前评估的IDH-B (10个变量).
- 光梯度增强机 (LightGBM),线性差异分析,支向量机,XGBoost,TabNet和多层感知器的性能比较.
主要成果:
- 轻GBM模型表现出卓越的性能,在IDH-A模型中的Fall30Nadir90定义中达到0.82的C统计,优于其他模型 (P<0.01).
- 在IDH-A模型中的LightGBM性能在各种IDH定义中从0.77到0.89不等.
- 在IDH-B模型中,LightGBM实现了0.68至0.78的C统计,也超过了其他方法.
结论:
- 机器学习,特别是LightGBM方法,为HD患者的IDH风险预测提供了一种高性能和可解释的方法.
- 确定了作为IDH发病率显著风险因素的关键变量.
- 开发的IDH-A和IDH-B模型可以相互补充,在不同的临床环境中促进及时干预.
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