Fardouly Jasmine, Crosby Ross D, Sukunesan Suku
School of Psychology, UNSW Sydney, Sydney, NSW, 2052, Australia.
Sanford Center for Biobehavioral Research, Sanford Research, Fargo, ND, USA.
J Eat Disord. 2022 May 8;10(1):66. doi: 10.1186/s40337-022-00581-2.
Advances in machine learning and digital data provide vast potential for mental health predictions. However, research using machine learning in the field of eating disorders is just beginning to emerge. This paper provides a narrative review of existing research and explores potential benefits, limitations, and ethical considerations of using machine learning to aid in the detection, prevention, and treatment of eating disorders. Current research primarily uses machine learning to predict eating disorder status from females' responses to validated surveys, social media posts, or neuroimaging data often with relatively high levels of accuracy. This early work provides evidence for the potential of machine learning to improve current eating disorder screening methods. However, the ability of these algorithms to generalise to other samples or be used on a mass scale is only beginning to be explored. One key benefit of machine learning over traditional statistical methods is the ability of machine learning to simultaneously examine large numbers (100s to 1000s) of multimodal predictors and their complex non-linear interactions, but few studies have explored this potential in the field of eating disorders. Machine learning is also being used to develop chatbots to provide psychoeducation and coping skills training around body image and eating disorders, with implications for early intervention. The use of machine learning to personalise treatment options, provide ecological momentary interventions, and aid the work of clinicians is also discussed. Machine learning provides vast potential for the accurate, rapid, and cost-effective detection, prevention, and treatment of eating disorders. More research is needed with large samples of diverse participants to ensure that machine learning models are accurate, unbiased, and generalisable to all people with eating disorders. There are important limitations and ethical considerations with utilising machine learning methods in practice. Thus, rather than a magical solution, machine learning should be seen as an important tool to aid the work of researchers, and eventually clinicians, in the early identification, prevention, and treatment of eating disorders.
机器学习和数字数据的进展为心理健康预测提供了巨大潜力。然而，在饮食失调领域使用机器学习的研究才刚刚开始出现。本文对现有研究进行了叙述性综述，并探讨了使用机器学习辅助饮食失调的检测、预防和治疗的潜在益处、局限性和伦理考量。当前的研究主要使用机器学习，通过女性对经过验证的调查、社交媒体帖子或神经影像数据的回答来预测饮食失调状况，通常具有较高的准确率。这项早期工作为机器学习改善当前饮食失调筛查方法的潜力提供了证据。然而，这些算法推广到其他样本或大规模使用的能力才刚刚开始被探索。机器学习相对于传统统计方法的一个关键优势是，它能够同时检查大量（数百到数千个）多模态预测因子及其复杂的非线性相互作用，但很少有研究在饮食失调领域探索这一潜力。机器学习还被用于开发聊天机器人，围绕身体形象和饮食失调提供心理教育和应对技能培训，这对早期干预具有重要意义。还讨论了使用机器学习来个性化治疗方案、提供生态瞬时干预以及辅助临床医生的工作。机器学习为准确、快速且经济高效地检测、预防和治疗饮食失调提供了巨大潜力。需要对大量不同参与者的样本进行更多研究，以确保机器学习模型准确、无偏差且能推广到所有饮食失调患者。在实践中使用机器学习方法存在重要的局限性和伦理考量。因此，机器学习不应被视为神奇的解决方案，而应被视为一种重要工具，以协助研究人员乃至临床医生在饮食失调的早期识别、预防和治疗方面的工作。
