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相关概念视频

Analgesia and Pain Management01:25

Analgesia and Pain Management

518
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...
518

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机器学习在疼痛神经调节中的作用

Tessa Harland1, Trish Elliott2, Ilknur Telkes3

  • 1Department of Neurosurgery, Albany Medical College, Albany, NY, USA.

Advances in experimental medicine and biology
|November 10, 2024
PubMed
概括

机器学习 (ML) 可以通过增强患者选择和优化神经调节疗法来改善疼痛管理. 解决ML算法和数据质量的挑战对于其在疼痛医学中的成功应用至关重要.

关键词:
生物标志物 生物标志物候选人的选择选择.慢性疼痛是一种慢性疼痛.机器学习是机器学习.疼痛神经调节是一种神经调节.优化编程的优化编程.脊髓刺激是一种脊髓刺激.

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科学领域:

  • 疼痛药,神经调节和机器学习 (ML) 的交集.
  • 人工智能在医疗保健和治疗干预中的应用.

背景情况:

  • 慢性疼痛管理面临局限性,特别是在选择适当的患者进行神经调节疗法,如脊髓刺激 (SCS).
  • 目前的限制导致高故障率和SCS的设备扩展,增加医疗保健成本.

研究的目的:

  • 探索机器学习技术如何解决疼痛神经调节的当前挑战.
  • 突出ML在改善患者选择,优化治疗计划和评估疼痛管理结果方面的潜力.
  • 讨论ML对疼痛神经调节领域的未来影响.

主要方法:

  • 在疼痛神经调节中的机器学习应用的审查和讨论.
  • 专注于用于患者分层的预测建模.
  • 探索用于实时治疗调整的自适应算法和用于客观评估的生物标志物识别.

主要成果:

  • 机器学习具有显著的潜力,可以提高患者对神经调节的选择,从而改善结果.
  • 基于ML的自适应算法可以优化设备编程,以满足个体患者的需求.
  • 通过ML识别客观生物标志物可以改善治疗疗效的评估.

结论:

  • 机器学习将通过提高神经调节疗法的疗效和效率来彻底改变疼痛管理.
  • 解决与算法透明度,数据质量和通用性相关的挑战对于ML在疼痛医学中的广泛采用至关重要.
  • 整合ML有望为慢性疼痛患者提供更个性化,更有效的治疗方法.