Deep learning for small and big data in psychiatry.
Deep learning for small and big data in psychiatry.
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DOI：
10.1038/s41386-020-0767-z
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发表时间：
2021-01
期刊：
影响因子：
--
通讯作者：
Durstewitz D
中科院分区：
文献类型：
--
作者：
Koppe G;Meyer-Lindenberg A;Durstewitz D
今天的精神病学必须更好地理解精神疾病背后的常见和独特的病理生理机制，以便提供更有效的个性化治疗。为此，使用传统统计方法对“小”实验样本进行分析，似乎在很大程度上未能捕捉到精神表型背后的异质性。机器学习的现代算法和方法，特别是深度学习，为解决这些问题提供了新的希望，因为它们在其他学科中具有出色的预测性能。深度学习算法的优势在于，它们可以有效地实现非常复杂的、原则上任意的预测-响应映射。这种能力是有代价的，需要大量的训练（和测试）样本来推断（有时超过数百万）模型参数。这似乎与迄今为止在精神病学人类研究中可用的相当“小”的样本（n < 10,000）以及在单个受试者水平上预测治疗的雄心（n = 1）不一致。在这里，我们的目标是对我们如何在精神病学中使用这些模型进行预测给出一个全面的概述。我们回顾了机器学习方法与更传统的统计假设驱动方法的比较，它们的复杂性如何与大样本量的需求相关，以及我们如何在精神神经科学中最佳地使用这些强大的技术。
Psychiatry today must gain a better understanding of the common and distinct pathophysiological mechanisms underlying psychiatric disorders in order to deliver more effective, person-tailored treatments. To this end, it appears that the analysis of ‘small’ experimental samples using conventional statistical approaches has largely failed to capture the heterogeneity underlying psychiatric phenotypes. Modern algorithms and approaches from machine learning, particularly deep learning, provide new hope to address these issues given their outstanding prediction performance in other disciplines. The strength of deep learning algorithms is that they can implement very complicated, and in principle arbitrary predictor-response mappings efficiently. This power comes at a cost, the need for large training (and test) samples to infer the (sometimes over millions of) model parameters. This appears to be at odds with the as yet rather ‘small’ samples available in psychiatric human research to date (n < 10,000), and the ambition of predicting treatment at the single subject level (n = 1). Here, we aim at giving a comprehensive overview on how we can yet use such models for prediction in psychiatry. We review how machine learning approaches compare to more traditional statistical hypothesis-driven approaches, how their complexity relates to the need of large sample sizes, and what we can do to optimally use these powerful techniques in psychiatric neuroscience.
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影响因子：
32.8
作者：
Bengio, Yoshua
通讯作者：
Bengio, Yoshua
影响因子：
3
作者：
Basar, E;Basar-Eroglu, C;Schürmann, M
通讯作者：
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影响因子：
2.9
作者：
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通讯作者：
Shirman, Sasha
DOI：
10.1073/pnas.1903070116
发表时间：
2019-08-06
影响因子：
11.1
作者：
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通讯作者：
Mandal, Soumik
影响因子：
8.1
作者：
Bergmeir, Christoph;Benitez, Jose M.
通讯作者：
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