Prediction of Opioid Cessation Using Machine Learning
The non-prescribed use of opioid analgesics has become a significant health and social problem. While studies had focused on opioid use disorder (OUD), a systematic inquiry of the lifestyle factors for opioid cessation has not been conducted. We performed supervised machine learning with feature selection on Yale-Penn dataset among subjects who met DSM-5 OUD criteria to predict current opioid use status. Subjects were interviewed by Semi-Structured Assessment for Drug Dependence and Alcoholism (SSADDA), a comprehensive diagnostic questionnaire. We observed moderate to high prediction efficiency using LASSO, SVM and random forest with SSADDA input alone. Future steps include using DNN and adding genetics from GWAS to see the improvement of accuracy.
Jiayi Wu Cox is a Ph. D candidate in Genetics and Genomics program at Boston University. Her research focuses on using bioinformatics and machine learning to find the genetic risk factors for a variety of diseases including addiction. In addition to her thesis, she also studies epigenetic changes in response to drug treatment and gene expression level differences due to disease phenotypes. Jiayi graduated from Tufts University with M.S degree in Pharmacology and Experimental Therapeutics. She hope her work can facilitate the development of personalized medicine, that people will be treated based on who they are genetically and the lifestyle they choose.