成年人抗发作药物治疗选择的演变:人工智能辅助抗发作药物选择是否准备好进入黄金时段?
Charlene L Gunasekera1, Joseph I Sirven1, Anteneh M Feyissa1
1Department of Neurology, Mayo Clinic, Jacksonville, FL, USA.
选择适合的抗发作药物 (ASM) 是一个复杂的问题. 人工智能 (AI) 和机器学习 (ML) 显示出预测ASM反应,个性化治疗的前景.
科学领域:
- 神经学 神经学
- 药理学 药理学是指药理学的学科.
- 人工智能的人工智能
背景情况:
- 抗发作药物 (ASM) 对于管理至关重要,其目的是以最小的副作用缓解发作.
- 选择ASM涉及许多患者和药物因素,通常需要广泛的临床专业知识.
- 目前的试错方法可能会导致患者长期治疗失败.
研究的目的:
- 强调在选择最佳抗发作药物方面需要改进的方法.
- 探索人工智能 (AI) 和机器学习 (ML) 在个性化药疗中的潜力.
- 讨论将网络障碍概念集成到AI模型中,以预测ASM响应.
主要方法:
- 审查当前的ASM选择策略在治疗中.
- 讨论针对个性化药物治疗的新兴AI和ML方法.
- 用于将网络属性集成到预测模型中的概念框架.
主要成果:
- 传统的ASM选择方法往往不理想,导致治疗延迟.
- 人工智能和机器学习为预测个体ASM疗效和耐受性提供了一个潜在的途径.
- 个性化网络可以增强ML模型的预测能力.
结论:
- 需要一种更可靠的方法来预测患者对抗发作药物的反应.
- 人工智能和机器学习,特别是深度学习,对于优化ASM选择具有重大潜力.
- 需要进一步的研究来将人工智能驱动的预测模型转化为药物治疗的常规临床实践.
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