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Machine Learning in Non-Medical Cannabis Use Study: A Review

Mingming Peng
Florida A&M University

Co-Author: Hongmei Chi1
1Florida A&M University

Non-medical cannabis uses has long been a widely discussed issue in human society. With the gradual legalization of non-medical cannabis uses in many countries and some states in the United States, research on the health effects of cannabis has regained attention from the public. In this context, the application of machine learning to address related issues has attracted much interest. We know that, with the popularity of IoT devices and smartphones, some of the ML architectures, including deep neural networks (DNN), convolutional neural networks (CNN), and recurrent neural networks (RNN), have been applied to fields such as digital health during the last decade. Many popular mHealth apps adopt Machine Learning algorithms. Therefore, we need to review the application and research progress of machine learning in non-medical cannabis use fields. This study presents a systematic review of how machine learning has been applied to the study of non-medical cannabis use over the past 10 years. We conducted a search of published literature from PubMed and IEEE Xplore and ACM digital Library.

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