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Updated: May 30, 2025

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在术后患者中多模式疼痛识别:机器学习方法

Ajan Subramanian1, Rui Cao2, Emad Kasaeyan Naeini1

  • 1Department of Computer Science, University of California, Irvine, Irvine, CA, United States.

JMIR formative research
|January 27, 2025
PubMed
概括

这项研究开发了一个机器学习框架,使用生物信号进行客观的术后疼痛评估. 多模式方法实现了超过80%的准确性,改善了临床环境中的疼痛监测.

关键词:
剧烈的急性疼痛是一种疼痛.行为疼痛 行为疼痛临床疼痛管理 临床疼痛管理电心电图 (ECG) 是一种心电图.电皮活动的电皮活动.一个电心图 (electromyogram) 是一个电心图.医疗保健 医疗保健 医疗保健机器学习方法的机器学习方法.基于机器学习的框架.多式联运信息融合是多式联运信息的融合.基于多模式机器学习的框架.疼痛评估疼痛的评估.疼痛的强度,疼痛的强度.识别疼痛强度,识别疼痛强度.疼痛的测量 疼痛的测量疼痛监测 疼痛监测 疼痛监测疼痛的识别 疼痛的识别自我报告的疼痛水平.信号处理是指信号处理的过程.监管能力较弱 监管能力较弱

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