Exponent, Inc.
Western Michigan University.
Subst Use Misuse. 2022;57(13):1982-1987. doi: 10.1080/10826084.2022.2125272. Epub 2022 Sep 21.
Transfer learning, which involves repurposing a trained model on a related task, may allow for better predictions with substance use data than models that are trained using the target data alone. This approach may also be useful for small clinical datasets. The current study examined a method of classifying substance use treatment success using transfer learning. Transfer learning was used to classify data from a nationwide database. We trained a convolutional neural network on a heroin use treatment dataset, then trained and tested on a smaller opioid use treatment dataset. We compared this model with a baseline model that did not benefit from transfer learning, and a tuned random forest (RF). The goal was to see if model weights transfer across related substances and from large to small datasets. The transfer model outperformed the RF model and baseline model. These findings suggest leveraging the power of large datasets for transfer learning may be an effective approach in predicting substance use disorder (SUD) treatment outcomes. It is possible to achieve a score that performs better than RF using transfer learning.
迁移学习，即将经过训练的模型应用于相关任务，可能会比仅使用目标数据进行训练的模型在物质使用数据上产生更好的预测结果。这种方法对于小型临床数据集也可能是有用的。本研究探讨了一种使用迁移学习对物质使用治疗成功进行分类的方法。迁移学习用于对来自全国性数据库的数据进行分类。我们在海洛因使用治疗数据集上训练卷积神经网络，然后在较小的阿片类药物使用治疗数据集上进行训练和测试。我们将该模型与未受益于迁移学习的基线模型和调优随机森林 (RF) 进行了比较。目的是观察模型权重是否在相关物质之间以及从大数据集到小数据集之间转移。迁移模型的表现优于 RF 模型和基线模型。这些发现表明，利用大型数据集进行迁移学习可能是预测物质使用障碍 (SUD) 治疗结果的有效方法。使用迁移学习可以获得比 RF 更好的分数。
