Unsupervised machine learning identifies opioid taper reversal patterns in a longitudinal cohort (2008-2018).

ABSTRACT: Chronic pain is commonly treated with long-term opioid therapy, but rapid opioid dose tapering has been associated with increased adverse events. Little is known about heterogeneity in the population of patients on high dose opioids and their response to different treatments. Our aim was to examine opioid dose management and other patient characteristics in a longitudinal, clinically diverse, national population of opioid dependent patients. We used spectral clustering, an unsupervised artificial intelligence (AI) approach, to identify patients in a national claims data warehouse who were on an opioid dose tapering regimen from 2008-2018. Due to the size and heterogeneity of our cohort, we did not impose any restrictions on the kind or number of clusters to be identified in the data. Of 11

SUBMITTER: Ray M 

PROVIDER: S-EPMC11975097 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature