可穿戴计算机
物理疗法
回廊的
评定量表
强度(物理)
物理医学与康复
医学
可穿戴技术
回忆偏差
计算机科学
金标准(测试)
疼痛管理
膝关节痛
心理学
骨关节炎
外科
嵌入式系统
替代医学
发展心理学
物理
病理
量子力学
内科学
作者
Sandi Wibowo,Wei Liang Chaw,Chris Wilson Antuvan,Hao Chen
标识
DOI:10.1109/embc40787.2023.10340199
摘要
Effective post-operative pain management requires an accurate and frequent assessment of the pain experienced by the patients. The current gold-standard of pain assessment is through patient self-evaluation (e.g., numeric rating scale, NRS) which is subjective, prone to recall-bias, and does not provide comprehensive information of the pain intensity and its trends. We conducted a study to explore the potential of wearable biosensors and machine learning-based analysis of physiological parameters to estimate the pain intensity. The results from our study of post-operative knee surgery patients monitored over a period of 30 days demonstrate the feasibility of the system in ambulatory setting, with a substantial agreement (Cohen's Kappa = 0.70, 95% CI 0.68-0.72) between the pain intensity estimation and the patient reported numerical rating scale. Therefore, the wearable biosensors coupled with the machine learning-derived pain estimation are capable of remotely assessing the pain intensity.
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