在诊断机器学习研究中,生活方式因素和其他常见精神障碍的预测因素:系统性审查

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Published on: January 11, 2020

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Emma Todd¹, Rebecca Orr¹, Elizabeth Gamage¹

¹Deakin University, Food & Mood Centre, Institute for Mental and Physical Health and Clinical Translation (IMPACT), Health Education and Research Building, Ryrie Street, Geelong, Victoria, Australia; Deakin University, Institute for Mental and Physical Health and Clinical Translation (IMPACT), 75 Pigdons Rd, Waurn Ponds, Victoria, Australia.

Computers in biology and medicine

|December 12, 2024

概括

机器学习模型可以预测常见的精神障碍 (CMD),但研究往往具有偏见的高风险. 生活方式数据在预测CMD方面未得到充分利用,这限制了可靠的预测因子识别.

科学领域:

计算精神病学是一种计算精神病学.
医疗保健中的人工智能
系统审查方法论系统审查方法论

背景情况:

机器学习 (ML) 模型为预测常见精神障碍 (CMD) 和识别可修改的风险因素提供了潜力.
机器学习可以通过突出关键的预测要素来为CMD提供有针对性的干预措施.
本综述综合了关于CMD预测的ML研究,评估性能和生活方式数据的作用.

研究的目的:

系统地审查和评估ML模型在预测常见精神障碍 (CMD) 的性能.
评估预测CMD的ML研究中的偏差风险.
确定将生活方式数据纳入ML模型中的CMD预测以及其他因素的潜力.

主要方法:

系统审查遵循PRISMA指导方针,搜索多个数据库 (MEDLINE,EMBASE,PsycInfo等). 在2024年8月之前.
包括的研究使用了具有特征重要性的ML方法来预测成年人中的CMD.
使用PROBAST评估偏差风险 (ROB); 模型性能指标进行比较; 顶级变量分类.

主要成果:

包括117项研究;深度学习方法在预测CMD病例方面表现出最高的准确性.
人口环境预测因素最常被确定在顶级变量列表中,通常与心理社会和生物因素相结合.
生活方式数据很少被用作唯一预测因素 (4.27%);研究通常表现出高异质性和ROB.

结论:

这次审查是首次评估CMD的诊断ML实用程序,ROB和预测器类型.
虽然使用ML可以预测CMD,但高ROB和生活方式数据的不足阻碍了强大的预测器集识别.
需要进一步的研究来利用包括生活方式因素在内的综合数据,以提高ML模型的准确性并减少CMD预测中的偏差.

关键词:

焦虑焦虑是一种焦虑.抑郁症抑郁症抑郁症生活方式生活方式机器学习机器学习预测者预测者

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