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一本关于使用机器学习从生物精神病学数据中提取知识的入门书

Thomas P Quinn¹, Jonathan L Hess², Victoria S Marshe^3,4

¹Applied Artificial Intelligence Institute (A2I2), Burwood, VIC, 3125, Australia.

Molecular psychiatry

|January 4, 2024

概括

机器学习 (ML) 越来越多地用于生物医学研究,特别是精神病学. 本综述为ML方法,其精神病学应用以及负责使用的报告标准 (GREMLIN) 提供了一份指南.

科学领域:

生物医学科学生物医学科学
精神病学是一个精神病学.
机器学习机器学习

背景情况:

机器学习 (ML) 应用在生物医学科学中的快速增长.
由协作,大数据集,可访问的分析和计算能力驱动.
对于数据科学家和生物医学科学家来说,需要对ML的能力和局限性进行教育.

研究的目的:

为生物医学受众提供ML的批判性审查,重点关注精神病学应用.
涵盖定义,常见的ML方法和精神病学的历史趋势.
提出报告标准 (GREMLIN) 和一个联盟 (MLPsych) 以促进精神病学中的ML.

主要方法:

审查现有的关于机器学习方法的文献.
在精神病学中应用ML的历史趋势的分析.
制定报告准则 (GREMLIN) 和一个财团提案 (MLPsych).

主要成果:

广泛使用的ML方法及其与精神病学相关性的全面概述.
在精神病学研究中识别ML采用的历史模式.
建立研究设计和报告的GREMLIN指南.
关于机器学习在精神病学 (MLPsych) 联盟的建议.

结论:

机器学习为推进生物精神病学提供了巨大的潜力.
标准化报告 (GREMLIN) 对于严格和可重复的ML研究至关重要.
拟议的MLPsych联盟旨在促进该领域的合作和创新.

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