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对“儿童和青少年精神病学的转化机器学习”的评论。

Commentary to "Translational machine learning for child and adolescent psychiatry".

机构信息

Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.

Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.

出版信息

J Child Psychol Psychiatry. 2022 Apr;63(4):444-446. doi: 10.1111/jcpp.13593. Epub 2022 Mar 7.

Abstract

In this commentary on 'Translational Machine Learning for Child and Adolescent Psychiatry,' by Dwyer and Koutsouleris, we summarize some of the main points made by the authors, which highlight the importance of emerging applications of machine learning for psychiatric disorders in youth but also emphasize principles of good practice. We also offer complementary insights regarding large-scale training, harmonization, and the ability of these artificial intelligence models to adapt to new datasets, which is critical for their stability across imaging centers, and hence for their widespread clinical adoption.

摘要

在对 Dwyer 和 Koutsouleris 的“儿童和青少年精神病学的转化机器学习”一文的评论中,我们总结了作者提出的一些要点,这些要点强调了机器学习在青年精神障碍中的新兴应用的重要性,但也强调了良好实践的原则。我们还提供了关于大规模培训、协调以及这些人工智能模型适应新数据集的能力的补充见解,这对于它们在成像中心之间的稳定性以及在广泛的临床应用中至关重要。

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