Predicting Potential Propensity of Adolescents to Drugs via New Semi-supervised Deep Ordinal Regression Model
Predicting Potential Propensity of Adolescents to Drugs via New Semi-supervised Deep Ordinal Regression Model
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DOI：
10.1007/978-3-030-59710-8_62
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发表时间：
2020-10
期刊：
影响因子：
3.4
通讯作者：
Alireza Ganjdanesh;Kamran Ghasedi;L. Zhan;Weidong (Tom) Cai;Heng Huang
中科院分区：
文献类型：
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作者：
Alireza Ganjdanesh;Kamran Ghasedi;L. Zhan;Weidong (Tom) Cai;Heng Huang
年轻人吸毒成瘾是现实世界中最严重的问题之一，它给他们的家庭和社会带来了巨大的经济和情感负担。因此，在较早的年龄预测潜在的吸毒倾向可以防止许多危害。在本文中，我们提出了一个新的半监督深度有序回归模型来预测青少年对大麻的可能倾向，该模型使用来自148个兴趣区域（roi）的扩散mri衍生平均扩散率（MD）来预测青少年对大麻的可能倾向。传统的深度有序回归模型不能直接应用于我们的生物医学问题，因为我们的生物医学问题只有少量的标记数据，不足以训练深度学习模型。因此，我们设计了一种深度有序回归的半监督学习机制，这样标记和未标记的数据都可以用来增强模型训练。在我们的实验中，我们使用ABCD数据集，其中包含被研究青少年的MRI图像以及他们在李克特量表中对包含大麻问题的问卷的回答。在ABCD数据集上的实验结果验证了新方法的优越性能。我们的研究提供了一种利用脑MRI数据预测药物倾向的廉价方法。
Addiction to drugs between young people is one of the most severe problems in the real world, and it imposes a huge financial and emotional burden on their families and societies. Therefore, predicting potential inclination to drugs at earlier ages can prevent lots of detriments. In this paper, we propose a new semi-supervised deep ordinal regression model to predict the possible propensity of adolescents to marijuana using the diffusion MRI-derived mean diffusivity (MD) from 148 Regions of Interest (ROIs). The traditional deep ordinal regression models cannot be directly applied to our biomedical problem which only has a small number of labeled data, not enough to train the deep learning models. Thus, we design a semi-supervised learning mechanism for deep ordinal regression, such that both labeled and unlabeled data can be used to enhance the model training. In our experiments, we use the ABCD dataset, which contains MRI images of the adolescents under study and their answers in the Likert scale to a questionnaire containing questions about Marijuana. Experimental results on the ABCD dataset validate the superior performance of our new method. Our study provides an inexpensive way to predict the drug tendency using brain MRI data.