Applied Artificial Intelligence Institute (A2I2), Burwood, VIC, 3125, Australia.
Department of Psychiatry and Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA.
Mol Psychiatry. 2024 Feb;29(2):387-401. doi: 10.1038/s41380-023-02334-2. Epub 2024 Jan 4.
Applications of machine learning in the biomedical sciences are growing rapidly. This growth has been spurred by diverse cross-institutional and interdisciplinary collaborations, public availability of large datasets, an increase in the accessibility of analytic routines, and the availability of powerful computing resources. With this increased access and exposure to machine learning comes a responsibility for education and a deeper understanding of its bases and bounds, borne equally by data scientists seeking to ply their analytic wares in medical research and by biomedical scientists seeking to harness such methods to glean knowledge from data. This article provides an accessible and critical review of machine learning for a biomedically informed audience, as well as its applications in psychiatry. The review covers definitions and expositions of commonly used machine learning methods, and historical trends of their use in psychiatry. We also provide a set of standards, namely Guidelines for REporting Machine Learning Investigations in Neuropsychiatry (GREMLIN), for designing and reporting studies that use machine learning as a primary data-analysis approach. Lastly, we propose the establishment of the Machine Learning in Psychiatry (MLPsych) Consortium, enumerate its objectives, and identify areas of opportunity for future applications of machine learning in biological psychiatry. This review serves as a cautiously optimistic primer on machine learning for those on the precipice as they prepare to dive into the field, either as methodological practitioners or well-informed consumers.
机器学习在生物医学科学中的应用正在迅速发展。这种增长的动力来自不同机构和跨学科的合作、大型数据集的公开可用性、分析程序的可访问性的提高以及强大计算资源的可用性。随着对机器学习的访问和接触的增加，人们有责任进行教育，更深入地了解其基础和局限性，这同样适用于希望在医学研究中运用分析工具的数据分析人员，以及希望利用这些方法从数据中获取知识的生物医学科学家。本文为具有生物医学背景的读者提供了对机器学习的通俗易懂的综述，以及它在精神病学中的应用。这篇综述涵盖了常用机器学习方法的定义和阐述，以及它们在精神病学中的使用历史趋势。我们还提供了一套标准，即神经精神药理学中报告机器学习研究的指南（GREMLIN），用于设计和报告使用机器学习作为主要数据分析方法的研究。最后，我们提议成立精神病学中的机器学习（MLPsych）联盟，列举其目标，并确定生物精神病学中机器学习未来应用的机会领域。对于那些准备投身该领域的人来说，这篇综述是一个谨慎乐观的入门指南，无论是作为方法实践人员还是有见识的消费者。
