开发和验证基于机器学习的预测模型,用于儿童中枢静脉接入装置相关的血栓形成
Maoling Fu1, Xinyu Li1, Zhuo Wang1
1Department of Nursing, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Road, Wuhan, Hubei 430030, China; School of Nursing, Tongji Medical College, Huazhong University of Science and Technology, 13 Aviation Road, Wuhan, Hubei 430030, China.
Thrombosis research
|January 31, 2025
概括
这项研究确定了儿童中枢静脉接入装置 (CVAD) 相关血栓形成的11个风险因素. 后勤回归模型有效预测CRT风险,帮助早期干预.
科学领域:
- 儿科血栓瘤研究研究
- 医疗信息学 医疗信息学
- 临床风险预测预测
背景情况:
- 与中央静脉接入装置 (CVAD) 相关的血栓形成 (CRT) 是一个显著的并发症.
- 缺乏有效的儿童CRT风险识别和评估工具.
- 早期检测和预防对于儿童的CRT管理至关重要.
研究的目的:
- 在儿科患者中确定CRT的关键风险因素.
- 开发和验证基于机器学习的儿童CRT预测模型.
- 提供预测和预防儿童CRT的基础.
主要方法:
- 从患有CVAD的儿科患者 (2018-2024) 收集回顾性和前性数据.
- 通过LASSO回归来确定CRT的独立风险因素.
- 开发和验证四种机器学习模型:物流回归 (LR),随机森林,人工神经网络和极端梯度增强.
主要成果:
- 在1445名儿科患者中,CRT的总发病率为17.4%.
- 确定了11个独立的风险因素,包括血栓形成史,白血病,导管特征和特定治疗方法.
- 在内部和外部验证中,LR模型表现出卓越的性能.
结论:
- 确定了儿科CRT的11个独立风险因素.
- 一种基于逻辑回归的预测模型显示了早期CRT风险评估的高临床适用性.
- 开发的模型为儿童CRT的预测和预防提供了有价值的支持.
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