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使用机器学习和心电图波形分析对子宫收缩引起的疼痛进行增强的分娩疼痛监测

Yuan-Chia Chu^1,2,3, Saint Shiou-Sheng Chen^4,5,6,7, Kuen-Bao Chen^8,9

¹Department of Information Management, Taipei Veterans General Hospital, Taipei, 11267, Taiwan, R.O.C.

|September 6, 2024

概括

这项研究开发了一种机器学习模型,使用心电图 (ECG) 数据来有效监测子宫收缩期间的产痛. 轻GBM模型实现了90%的准确性,提供了一种可靠的疼痛评估方法.

科学领域:

生物医学工程生物医学工程
医疗保健中的机器学习
疼痛管理疼痛管理

背景情况:

临床试验.gov 标识符:NCT04461704.gov 临床试验
产痛监测对于母亲的护理至关重要.
当前的方法在持续评估方面可能存在局限性.

研究的目的:

开发一种创新的方法来使用心电图波形分析监测分娩疼痛.
使用机器学习 (ML) 来评估子宫收缩引起的疼痛.
创建一个基于心电图的知觉监测指数 (NoM).

主要方法:

从接受自然自发分娩 (NSD) 的妇女收集了6010个心电图样本.
开发和评估了包括LightGBM,XGBoost,SnapLogisticRegression和SnapDecisionTree在内的ML模型.
使用网格搜索和五倍交叉验证优化了超参数.

主要成果:

轻GBM模型实现了0.96的AUC和90%的准确性,优于其他模型.
XGBoost和SnapLogisticRegression模型也表现出强的表现,AUC从0.88到0.95.9之间.
使用ML进行心电图波形分析,证明对分娩疼痛监测有效.

结论:

将ML算法与心电图数据集成,显著提高了产痛监测的准确性和可靠性.
轻GBM模型在分娩期间持续监测疼痛时表现出极高的精度和稳定性.
这种方法在更广泛的医疗保健环境中对疼痛评估具有潜在的适用性.

关键词:

人工智能 (AI) 是一种人工智能.临床决策 - 临床决策电心电图 (ECG) 是一种心电图.医疗保健技术技术医疗保健技术劳动力疼痛劳动力疼痛机器学习机器学习没有意识的评估评估.疼痛监测疼痛监测疼痛监测预测分析是一种预测分析.子宫收缩子宫收缩

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