TY - GEN
T1 - Opioid relapse prediction with GaN
AU - Yang, Zhou
AU - Nguyen, Long H.
AU - Jin, Fang
PY - 2019/8/27
Y1 - 2019/8/27
N2 - Opioid addiction is a severe public health threat in the U.S, causing massive deaths and many social problems. Accurate relapse prediction is of practical importance for recovering patients since relapse prediction promotes timely relapse preventions that help patients stay clean. In this paper, we introduce a Generative Adversarial Networks (GAN) model to predict the addiction relapses based on sentiment images and social influences. Experimental results on real social media data from Reddit.com demonstrate that the GAN model delivers a better performance than comparable alternative techniques. The sentiment images generated by the model show that relapse is closely connected with two emotions ‘joy’ and ‘negative’. This work is one of the first attempts to predict relapses using massive social media (Reddit.com) data and generative adversarial nets. The proposed method, combined with knowledge of social media mining, has the potential to revolutionize the practice of opioid addiction prevention and treatment.
AB - Opioid addiction is a severe public health threat in the U.S, causing massive deaths and many social problems. Accurate relapse prediction is of practical importance for recovering patients since relapse prediction promotes timely relapse preventions that help patients stay clean. In this paper, we introduce a Generative Adversarial Networks (GAN) model to predict the addiction relapses based on sentiment images and social influences. Experimental results on real social media data from Reddit.com demonstrate that the GAN model delivers a better performance than comparable alternative techniques. The sentiment images generated by the model show that relapse is closely connected with two emotions ‘joy’ and ‘negative’. This work is one of the first attempts to predict relapses using massive social media (Reddit.com) data and generative adversarial nets. The proposed method, combined with knowledge of social media mining, has the potential to revolutionize the practice of opioid addiction prevention and treatment.
KW - Generative adversarial nets
KW - Opioid addicts detection
KW - Opioid relapse prediction
UR - http://www.scopus.com/inward/record.url?scp=85071635737&partnerID=8YFLogxK
U2 - 10.1145/3341161.3342951
DO - 10.1145/3341161.3342951
M3 - Conference contribution
T3 - Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019
SP - 560
EP - 567
BT - Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019
A2 - Spezzano, Francesca
A2 - Chen, Wei
A2 - Xiao, Xiaokui
PB - Association for Computing Machinery, Inc
T2 - 11th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019
Y2 - 27 August 2019 through 30 August 2019
ER -