根据皮肤潜在信号识别儿童的疼痛
Yubo Li1,2, Jiadong He1, Cangcang Fu3
1College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China.
Sensors (Basel, Switzerland)
|August 12, 2023
概括
这项研究引入了一种新的方法,用于客观评估儿童的疼痛,使用皮肤潜在信号和机器学习. 虽然达到70.63%的准确性,但需要进一步的研究来改善临床环境中的疼痛识别.
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
- 疼痛管理研究 疼痛管理研究
背景情况:
- 由于沟通障碍,儿童的疼痛评估具有挑战性.
- 目前的主观疼痛量表在客观性和可靠性方面存在局限性.
- 开发客观的疼痛指标对于有效的儿科疼痛管理至关重要.
研究的目的:
- 开发和验证一种使用皮肤潜在信号的新型疼痛评估方案.
- 将主观的疼痛体验转化为客观的,机器可学习的指标.
- 探索机器学习算法用于自动疼痛识别的有效性.
主要方法:
- 设计了一种便携式的非侵入性设备,用于测量皮肤潜在信号.
- 收集了623名受试者的数据,有358个有效记录 (218个静音,262个疼痛样本).
- 提取了38个特征,确定了7个关键特征,并应用了随机森林分类.
主要成果:
- 随机森林算法实现了70.63%的疼痛识别精度.
- 七个特定特征在区分疼痛和非疼痛状态方面表现出卓越的表现.
- 结果表明客观疼痛评估的潜力,尽管目前的准确性低于最先进的水平 (81.5%).
结论:
- 拟议的基于皮肤潜力的疼痛评估方案显示了在临床环境中客观疼痛识别的前景.
- 经过手术引起的临床疼痛刺激在控制强度方面存在挑战,影响准确性.
- 该方法的进一步改进是有必要的,以提高疼痛管理的准确性和临床实用性.
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