Machine learning approaches are just emerging in eating disorders research. Promising early results suggest that such approaches may be a particularly promising and fruitful future direction. However, there are several challenges related to the nature of eating disorders in building robust, reliable and clinically meaningful prediction models. This article aims to provide a brief introduction to machine learning and to discuss several such challenges, including issues of sample size, measurement, imbalanced data and bias; I also provide concrete steps and recommendations for each of these issues. Finally, I outline key outstanding questions and directions for future research in building, testing and implementing machine learning models to advance our prediction, prevention, and treatment of eating disorders.

中文翻译：

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机器学习推进饮食失调的预测、预防和治疗

机器学习方法刚刚出现在饮食失调研究中。有希望的早期结果表明，这种方法可能是一个特别有前途和富有成果的未来方向。然而，在构建稳健、可靠且具有临床意义的预测模型方面存在一些与饮食失调的性质相关的挑战。本文旨在简要介绍机器学习并讨论几个此类挑战，包括样本量、测量、数据不平衡和偏差等问题；我还为这些问题中的每一个提供了具体的步骤和建议。最后，我概述了在构建、测试和实施机器学习模型以推进我们对饮食失调的预测、预防和治疗方面的未来研究的关键突出问题和方向。