Challenges and promises for translating computational tools into clinical practice.
Challenges and promises for translating computational tools into clinical practice.
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DOI：
10.1016/j.cobeha.2016.02.001
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发表时间：
2016-10-01
影响因子：
5
通讯作者：
Busemeyer JR
中科院分区：
文献类型：
--
作者：
Ahn WY;Busemeyer JR
计算模型和相关方法极大地增进了我们对复杂行为和精神疾病背后的认知和神经生物学的理解。然而，还没有计算方法成功地转化为临床环境。本综述讨论了三个主要的方法论和实践挑战（A. 潜在神经认知过程的精确表征，B. 开发最佳分析方法，C. 开发大规模纵向研究并从多模态数据生成预测）以及数学心理学、计算神经科学、计算机科学和统计学等各个领域开发的潜在前景和工具。最后，我们强调跨学科沟通和协作的强烈需求。
Computational modeling and associated methods have greatly advanced our understanding of cognition and neurobiology underlying complex behaviors and psychiatric conditions. Yet, no computational methods have been successfully translated into clinical settings. This review discusses three major methodological and practical challenges (A. precise characterization of latent neurocognitive processes, B. developing optimal assays, C. developing large-scale longitudinal studies and generating predictions from multi-modal data) and potential promises and tools that have been developed in various fields including mathematical psychology, computational neuroscience, computer science, and statistics. We conclude by highlighting a strong need to communicate and collaborate across multiple disciplines.