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Predicting Opioid Use Outcomes in Minoritized Communities by Goyal, Abhay; Parekh, Nimay; Cheung, Lam Yin; Saha, Koustuv; Altice, Frederick L; O'hanlon, Robin; Man, Roger Ho Chun; Poellabauer, Christian; Guarino, Honoria; Gelabert, Pedro Mateu; Kumar, Navin is a scholarly article available to read on EtoBox.

What is Predicting Opioid Use Outcomes in Minoritized Communities about?

Machine learning algorithms can sometimes exacerbate health disparities based on ethnicity, gender, and other factors. There has been limited work at exploring potential biases within algorithms deployed on a small scale, and/or within minoritized communities. Understanding the nature of potential biases may improve the prediction of various health outcomes. As a case study, we used data from a sample of 539 young adults from minoritized communities who engaged in nonmedical use of prescription opioids and/or heroin. We addressed the indicated issues through the following contributions: 1) Using machine learning techniques, we predicted a range of opioid use outcomes for participants in our dataset; 2) We assessed if algorithms trained only on a majority sub-sample (e.g., Non-Hispanic/Latino, male), could accurately predict opioid use outcomes for a minoritized sub-sample (e.g., Latino, female). Results indicated that models trained on a random sample of our data could predict a range of opioid use outcomes with high precision. However, we noted a decrease in precision when we trained our models on data from a majority sub-sample, and tested these models on a minoritized sub-sample

Author

Goyal, Abhay; Parekh, Nimay; Cheung, Lam Yin; Saha, Koustuv; Altice, Frederick L; O'hanlon, Robin; Man, Roger Ho Chun; Poellabauer, Christian; Guarino, Honoria; Gelabert, Pedro Mateu; Kumar, Navin

Published

2023

Language

EN