Detection of Opioid Addicts via Attention-based bidirectional Recurrent Neural Network

Bibliographic Details
Main Author:	Wang, Yuchen
Language:	English
Published:	Case Western Reserve University School of Graduate Studies / OhioLINK 2020
Subjects:	Computer Science
Online Access:	http://rave.ohiolink.edu/etdc/view?acc_num=case1592255095863388

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spelling	ndltd-OhioLink-oai-etd.ohiolink.edu-case15922550958633882021-08-03T07:15:21Z Detection of Opioid Addicts via Attention-based bidirectional Recurrent Neural Network Wang, Yuchen Computer Science Opioid epidemic referring to the growing deaths and hospitalizations because of overdose of opioid usage and addiction. It has become a severe health problem in the United States. Many strategies have been developed by the federal and local governments and health communities to combat this crisis. Among them, improving our understanding of the epidemic through better health surveillance is one of the top priorities. In addition to direct testing, we can also gain insights about opioid addiction by analyzing data from social media because many drug users may choose not to do the tests. Instead, many of them tend to share their thoughts and experiences on social networks. In this paper, we take advantage of recent advances in machine learning, collect and analyze user posts from a popular social network Reddit with the goal to identify drug users. Posts from more than 1,000 users who have posted on three sub-reddits over a period of one month have been collected. In addition to the ones that contain key words such as opioid, opiate, or heroin, we have also collected posts that contain slang words of opioid such as black or chocolate. We propose an attention-based bidirectional long short memory model to identify opioid addicts. Experimental results show that the F1-score of our proposed method has an improvement of 5.7% to 15.2%, comparing with baseline algorithms. Furthermore, the model allows us to extract most informative words, such as opiate, fentanyl, and dose, from posts via the attention layer, which provides more insight how the machine learning algorithm works in distinguishing drug users from non drug users. 2020-09-07 English text Case Western Reserve University School of Graduate Studies / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=case1592255095863388 http://rave.ohiolink.edu/etdc/view?acc_num=case1592255095863388 unrestricted This thesis or dissertation is protected by copyright: some rights reserved. It is licensed for use under a Creative Commons license. Specific terms and permissions are available from this document's record in the OhioLINK ETD Center.
collection	NDLTD
language	English
sources	NDLTD
topic	Computer Science
spellingShingle	Computer Science Wang, Yuchen Detection of Opioid Addicts via Attention-based bidirectional Recurrent Neural Network
author	Wang, Yuchen
author_facet	Wang, Yuchen
author_sort	Wang, Yuchen
title	Detection of Opioid Addicts via Attention-based bidirectional Recurrent Neural Network
title_short	Detection of Opioid Addicts via Attention-based bidirectional Recurrent Neural Network
title_full	Detection of Opioid Addicts via Attention-based bidirectional Recurrent Neural Network
title_fullStr	Detection of Opioid Addicts via Attention-based bidirectional Recurrent Neural Network
title_full_unstemmed	Detection of Opioid Addicts via Attention-based bidirectional Recurrent Neural Network
title_sort	detection of opioid addicts via attention-based bidirectional recurrent neural network
publisher	Case Western Reserve University School of Graduate Studies / OhioLINK
publishDate	2020
url	http://rave.ohiolink.edu/etdc/view?acc_num=case1592255095863388
work_keys_str_mv	AT wangyuchen detectionofopioidaddictsviaattentionbasedbidirectionalrecurrentneuralnetwork
_version_	1719457376364396544

Detection of Opioid Addicts via Attention-based bidirectional Recurrent Neural Network

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