@inproceedings{63cae7dcff984e96baca4d53a93a37ec,
title = "Inferring users' usage patterns for drug abuse surveillance",
abstract = "Inferring drug usage patterns includes age of drug abuse and intention of rehabilitation, which is of much importance for drug abuse surveillance. The challenges are how to mine patterns from posts and interaction relationships between users. In this paper, we propose a novel drug usage pattern inference method, which improves the inference accuracy by integrating the semantic features and interaction relationships effectively. Experimental results on a real-world dataset demonstrate the efficacy of the proposed method.",
keywords = "Drug Abuse Surveillance, Interaction relationship, Semantic information, Usage Patterns Inference",
author = "Ruoran Liu and Qiudan Li and {Dajun Zeng}, Daniel",
year = "2019",
month = jul,
doi = "10.1109/ISI.2019.8823475",
language = "English (US)",
series = "2019 IEEE International Conference on Intelligence and Security Informatics, ISI 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "134--136",
editor = "Xiaolong Zheng and Ahmed Abbasi and Michael Chau and Alan Wang and Lina Zhou",
booktitle = "2019 IEEE International Conference on Intelligence and Security Informatics, ISI 2019",
note = "17th IEEE International Conference on Intelligence and Security Informatics, ISI 2019 ; Conference date: 01-07-2019 Through 03-07-2019",
}