Name
Title | ||
|---|---|---|
Joint prediction of cocaine craving and euphoria using structured prediction energy networks |
Abstract | ||
|---|---|---|
ABSTRACTIn recent years, wearable and mobile health sensing technologies have been developed to track drug usage and monitor different addiction-related states, including craving and euphoria. These states are interdependent and correlated, which is well documented in the literature. However, the state of the art digital biomarker technologies model these states independent of each other and thus fail to use the inherent relationship while making predictions. In our current work, we demonstrate how structured prediction energy networks (SPENs) can be used to capture the correlation and dependencies between self-reported craving, euphoria, and the underlying physiological biomarkers. More specifically, we use SPENs to jointly predict self-reported visual analog scale (VAS) ratings of cocaine craving and euphoria from cardiac signals captured from a wearable chest band. The proposed SPEN-based model can improve the performance of both VAS craving and VAS euphoria prediction by a Normalized Root Mean Square Error of respectively 4.6\% and 5.4\%. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3469266.3469881 | MOBISYS |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bhanu Teja Gullapalli | 1 | 0 | 0.34 |
| Gustavo A. Angarita | 2 | 0 | 0.34 |
| Deepak Ganesan | 3 | 3914 | 376.82 |
| Tauhidur Rahman | 4 | 143 | 12.62 |