Opioid Abuse Prediction Based on Multi-output Support Vector Regression

   @inproceedings{10.1145/3340997.3341006,
       author = {Gong, Haifan and Qian, Chaoqin and Wang, Yue and Yang, Jianfeng and Yi, Sheng and Xu, Zichen},
       title = {Opioid Abuse Prediction Based on Multi-Output Support Vector Regression},
       year = {2019},
       isbn = {9781450363235},
       publisher = {Association for Computing Machinery},
       address = {New York, NY, USA},
       url = {https://doi.org/10.1145/3340997.3341006},
       doi = {10.1145/3340997.3341006},
       abstract = {Opioid drug abuse has a negative impact on national health and social-economic development. It is essential to provide a solid analysis on the use of drug, efficiently. In this paper, we propose a method for drug use prediction and control. We started with a correlation analysis on historic data on opioid accounting from several states based on K-means cluster- ing. Based on heuristics, we propose our prediction model for opioid accounting based on Multi-output Support Vec- tor Regression (MSVR) while considering population fac- tors. We evaluate our method using drug data in 2017 with several state-of-the-practice baselines. Our proposed MSVR model performs 18\% better than the state-of-the-practice ARIMA model on Euclidean loss. Our MSVR model can effectively predict short-term trend of opioid abuse, which can be adopted to opioid abuse prevention.},
       booktitle = {Proceedings of the 2019 4th International Conference on Machine Learning Technologies},
       pages = {36–41},
       numpages = {6},
       keywords = {K-means, Canonical Correlation Analysis, Prediction, ARIMA, Opioid Abuse, MSVR},
       location = {Nanchang, China},
       series = {ICMLT 2019}
   }