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相关概念视频

Analgesia and Pain Management01:25

Analgesia and Pain Management

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Pain is critical to various clinical pathologies, provoking an urgent need for effective management. Pain, whether acute or chronic, is a complex neurochemical process. Its alleviation depends on the type, with nonopioid analgesics effective for mild to moderate pain, such as musculoskeletal or inflammatory pain, while neuropathic pain responds best to anticonvulsants, tricyclic antidepressants, or serotonin/norepinephrine reuptake inhibitors. For severe acute or chronic pain, opioids may be...
557

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Updated: Jun 18, 2025

Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity
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基于神经网络的疼痛强度估计中的不确定性量化.

Burcu Ozek1, Zhenyuan Lu1, Srinivasan Radhakrishnan1

  • 1Mechanical and Industrial Engineering Department, Northeastern University, Boston, Massachusetts, United States of America.

PloS one
|August 1, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的神经网络方法,通过提供间隔和量化不确定性来估计疼痛强度,从而改善临床决策. 修改后的下限和上限估计 (LossS) 算法,特别是在混合模型中,提供了最精确的疼痛评估.

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科学领域:

  • 生物医学工程 生物医学工程
  • 机器学习 机器学习
  • 疼痛管理 疼痛管理

背景情况:

  • 不适当的疼痛管理有严重的后果,包括降低生活质量和增加阿片类药物依赖的风险.
  • 客观的疼痛强度评估对于有效的干预至关重要,但由于个体的变化而具有挑战性.
  • 现有的机器学习方法用于疼痛估计,往往侧重于点估计,忽视数据和模型不确定性.

研究的目的:

  • 开发一种基于神经网络的方法,用于客观的疼痛间隔估计和不确定性量化.
  • 为临床医生提供更全面的了解患者疼痛强度,以便更好地做出决策.
  • 为了比较不同算法和模型构建方法对疼痛间隔估计的有效性.

主要方法:

  • 探索了三个算法:启动,通过遗传算法进行下限和上限估计 (LossL),并通过梯度下降修改下限和上限估计 (LossS).
  • 使用通用,个性化和混合 (基于集群) 模型构建方法评估模型性能.
  • 在各种覆盖概率 (50%,75%,85%,95%) 中评估了预测间隔宽度.

主要成果:

  • 修改后的下限和上限估计 (LossS) 算法通过提供相对于bootstrap和LossL.L.相比显著更窄的预测间隔来证明卓越的性能.
  • 在不同的覆盖概率中,LossS实现了高达26.9%的平均间隔宽度减少,与引导相比,和22.4%的LossL相比.
  • 混合模型构建方法,利用个体集群,在疼痛间隔估计中产生了最佳性能.

结论:

  • 建议的神经网络方法用于疼痛间隔估计有效量化不确定性,提供一个更有信息的替代点估计.
  • 损失S算法,特别是在混合模型框架内,代表了客观和可靠的疼痛强度评估的重大进步.
  • 这种方法有可能增强临床决策和改善患者在疼痛管理方面的结果.