부산대학교 도서관

Opioid Use Disorder is an international crisis, affecting many populations. Deep learning models can potentially predict opioid use disorder, but provide little insight to how predictions are derived. Impact scores, a new development in explainable artificial intelligence, measure how individual features affect deep learning outcomes. We modeled clinical visits to predict opioid use disorder, computed impact scores, and compared them to odds log ratios from logistic regression. Impact scores were generally comparable to odds log ratios, in providing insight to opioid abuse risk, but from a better-performing method than logistic regression.