A Novel GAN Approach to Augment Limited Tabular Data for Short-Term Substance Use Prediction

TL;DR

This paper introduces a novel GAN-based method to augment limited tabular survey data of PWUDs, significantly improving short-term substance use prediction accuracy for various drugs.

Contribution

The paper presents a new GAN approach tailored for high-dimensional, low-sample-size tabular data with survey skip logic, enhancing predictive models for substance use behaviors.

Findings

Augmented data improved AUROC by up to 13.4% for usage increase prediction.

Augmented data improved AUROC by up to 15.8% for usage frequency prediction.

Outperformed existing state-of-the-art generative models.

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.