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从计算到临床从计算到临床

Sarah W Yip1, Deanna M Barch2, Henry W Chase3

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此摘要是机器生成的。

计算精神病学为理解精神疾病提供了潜力,但将计算结果转化为临床仍然具有挑战性. 本综述确定了障碍,并提出了计算模型有效临床翻译的策略.

关键词:
认知神经科学是一种认知神经科学.计算精神病学是一种计算精神病学.机器学习 机器学习神经成像是一种神经成像.强化学习是一种强化学习.

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科学领域:

  • 计算精神病学是一种计算精神病学.
  • 翻译神经科学是一种神经科学.
  • 认知和情感建模的模型

背景情况:

  • 精神病学中的计算方法显示出阐明疾病机制的前景.
  • 目前的实用性主要在于逆向翻译 (从临床到计算),将疾病映射到计算过程中.
  • 这些模型的前向翻译 (计算到临床) 是有限的,有不清楚的障碍和解决方案.

研究的目的:

  • 确定认知和影响的计算模型在临床前和临床翻译方面的挑战.
  • 讨论克服这些已识别的翻译障碍的实际策略.

主要方法:

  • 视角审查综合了基础研究人员,计算科学家和临床医生的专家意见.
  • 对计算模型预测精神疾病治疗反应的能力现有证据的分析.
  • 讨论加强临床相关性和利益相关者的采用方面的考虑.

主要成果:

  • 确定了将计算模型从临床前系统转化为临床应用的具体挑战.
  • 提出了克服计算精神病学翻译障碍的实际方法.
  • 突出了计算模型预测治疗结果的证据,并讨论了最大限度地提高临床效用.

结论:

  • 解决翻译障碍对于实现计算精神病学的全部潜力至关重要.
  • 纵向测试和成本效益分析等策略可以提高临床相关性和采用率.
  • 需要进一步的研究和合作,以弥合计算模型和临床实践之间的差距.