{"type":"ScholarlyArticle","title":"A Novel GAN Approach to Augment Limited Tabular Data for Short-Term Substance Use Prediction","authors":[{"name":"Nguyen Thach","slug":"nguyen-thach","given":"Nguyen","family":"Thach","affiliation":"University of Nebraska-Lincoln"},{"name":"Patrick Habecker","slug":"patrick-habecker","given":"Patrick","family":"Habecker","affiliation":"University of Nebraska-Lincoln"},{"name":"Bergen Johnston","slug":"bergen-johnston","given":"Bergen","family":"Johnston","affiliation":"University of Nebraska-Lincoln"},{"name":"Lillianna Cervantes","slug":"lillianna-cervantes","given":"Lillianna","family":"Cervantes","affiliation":"University of Nebraska-Lincoln"},{"name":"Anika Eisenbraun","slug":"anika-eisenbraun","given":"Anika","family":"Eisenbraun","affiliation":"University of Nebraska-Lincoln"},{"name":"Alex Mason","slug":"alex-mason","given":"Alex","family":"Mason","affiliation":"University of Nebraska-Lincoln"},{"name":"Kimberly Tyler","slug":"kimberly-tyler","given":"Kimberly","family":"Tyler","affiliation":"University of Nebraska-Lincoln"},{"name":"Bilal Khan","slug":"bilal-khan","given":"Bilal","family":"Khan","affiliation":"Lehigh University"},{"name":"Hau Chan","slug":"hau-chan","given":"Hau","family":"Chan","affiliation":"University of Nebraska-Lincoln"}],"abstract":"Substance use is a global issue that negatively impacts millions of persons who use drugs (PWUDs). In practice, identifying vulnerable PWUDs for efficient allocation of appropriate resources is challenging due to their complex use patterns (e.g., their tendency to change usage within months) and the high acquisition costs for collecting PWUD-focused substance use data. Thus, there has been a paucity of machine learning models for accurately predicting short-term substance use behaviors of PWUDs. In this paper, using longitudinal survey data of 258 PWUDs in the U.S. Great Plains collected by our team, we design a novel GAN that deals with high-dimensional low-sample-size tabular data and survey skip logic to augment existing data to improve classification models' prediction on (A) whether the PWUDs would increase usage and (B) at which ordinal frequency they would use a particular drug within the next 12 months. Our evaluation results show that, when trained on augmented data from our proposed GAN, the classification models improve their predictive performance (AUROC) by up to 13.4% in Problem (A) and 15.8% in Problem (B) for usage of marijuana, meth, amphetamines, and cocaine, which outperform state-of-the-art generative models.","doi":"10.24963/ijcai.2024/827","url":"https://ijcai.org/proceedings/2024/827","pdf":"https://ijcai.org/proceedings/2024/0827.pdf","markdown":"https://ijcai.org/proceedings/2024/827.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/827","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"AI for Good","track":"AI for Good","number":827,"pages":{"first":7474,"last":7482},"keywords":["Machine Learning: General","Multidisciplinary Topics and Applications: General"],"videos":[],"references":[{"position":1,"raw":"[Andersson et al., 2023] Helle Wessel Andersson, Mats P Mosti, and Trond Nordfjaern. 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