基于机器学习的血液透析患者内日分析高血压的预测模型
Yu Wang1, Hongming Zhou2, Qi Guo3
1Department of Emergency, School of Medicine, Hangzhou Geriatric Hospital, Affiliated Hangzhou First People's Hospital Chengbei Campus, Westlake University, Hangzhou, 310005, China.
Journal of medical systems
|September 11, 2025
概括
机器学习模型预测血液透析 (HD) 患者的内透析性高血压 (IDH) 风险. 光梯度增强机 (LGBM) 算法在识别患有这种并发症高风险的患者方面表现出卓越的表现.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學.
- 生物医学工程 生物医学工程
- 数据科学数据科学数据科学
背景情况:
- 慢性病 (CKD) 和末期病 (ESRD) 构成了日益严重的全球健康挑战.
- 血液透析 (HD) 是一个关键的治疗方法,但内透析性高血压 (IDH) 是一个严重的并发症.
- 未经管理的IDH可以导致严重的心血管和神经系统问题.
研究的目的:
- 开发一种机器学习 (ML) 驱动的预警系统,用于预测IDH风险.
- 为了使临床医生能够识别高风险的HD患者进行有针对性的监测.
- 整合人口统计数据和透析会话记录,用于预测建模.
主要方法:
- 开发了两种临床预测模型 (IDH-1和IDH-2),使用光梯度增强机 (LGBM),支持矢量机 (SVM) 和Tab.Net.
- IDH-1使用透析前的生命体和治疗史来预测即时风险.
- IDH-2使用实时透析参数和历史生物标志物预测随后的疗程风险.
主要成果:
- 在LGBM模型中,IDH-1 (AUC:0.87) 和IDH-2 (AUC:0.74) 的表现都比较出色.
- IDH-1的主要预测因素包括透析前的透析压和历史的IDH发作.
- IDH-2的关键预测因素包括历史的IDH情节和透析后的缩压.
结论:
- 对于预测日内分析高血压而言,LGBM模型具有卓越的区分能力.
- 拟议的系统提供了一个可扩展的框架,用于个性化的HD患者风险分层.
- 早期识别IDH风险可能会减轻血液透析患者群体的不良结果.
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