用生物信号波形预测手术内低血压的机器学习方法
Jae-Geum Shim1, Wonhyuck Yoon2, Sang Jun Lee3
1Department of Anesthesiology and Pain Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul 03181, Republic of Korea.
Medicina (Kaunas, Lithuania)
|November 27, 2025
概括
这项研究开发了机器和深度学习模型,以使用患者生物信号和临床数据预测手术内低血压 (IOH),实现高精度. 这些人工智能模型旨在实现手术室的实时IOH预测,减少患者的并发症.
科学领域:
- 麻醉学 麻醉学
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 术内低血压 (IOH) 是与不良术后结果相关的关键因素,包括心肌梗塞,急性损伤和死亡率.
- 预测和预防IOH对于改善非心脏手术期间的患者安全至关重要.
研究的目的:
- 开发和验证机器学习 (ML) 和深度学习 (DL) 模型,用于预测手术内低血压.
- 利用手术内生物信号和个性化临床信息的组合来准确预测IOH.
主要方法:
- 一项使用VitalDB开放数据集的回顾性观察性研究,包括2611名接受非心脏手术的患者.
- 开发和验证ML (渐变增强机) 和DL (CNN-RNN) 模型,使用四种波形 (动脉血压,心电图,PPG,脑图) 和临床数据.
- 在低血压事件发生前5分钟评估模型性能.
主要成果:
- 梯度增强机和混合CNN-RNN模型都实现了0.94.94的接收器操作特征曲线 (AUROC) 下的高面积.
- 这些模型显示出强大的预测准确度,预测IOH的整体准确度为0.88.
- 这些模型使用波形数据和临床共变量成功预测了IOH.
结论:
- 经过验证的ML和DL模型可以有效地使用生物信号和临床数据预测手术内低血压.
- 这些预测模型有望在手术室实时检测IOH.
- 预计该研究的结果将有助于减少IOH的发病率和相关的患者发病率.
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