Skip to main content
Surgical Informatics Lab
Primary menu
  • Research
  • People
  • Publications
  • Webinars
  • Resources
  • Open Positions

Who doesn’t fit? A multi-institutional study using machine learning to uncover the limits of opioid prescribing guidelines

Justin, Yu, Jayson S Marwaha, Chris J Kennedy, Kortney A Robinson, Aaron Fleishman, Brendin R Beaulieu-Jones, Josh Bleicher, Lyen C Huang, Peter Szolovits, and Gabriel A Brat. 2022. “Who Doesn’t Fit? A Multi-Institutional Study Using Machine Learning to Uncover the Limits of Opioid Prescribing Guidelines”. Surgery 172 (2): 655-62.
Last updated on 02/21/2025

Recent Publications

  • Is More Thinking Always Better? First Impressions of ChatGPT-5 in Surgery Conversations
  • Development of a Claims-Based Computable Phenotype for Ulcerative Colitis Flares
  • Evaluating Capabilities of Large Language Models: Performance of GPT4 on Surgical Knowledge Assessments
  • Implications of mappings between International Classification of Diseases clinical diagnosis codes and Human Phenotype Ontology terms
  • Response to: Comment on “Integrating Human Intuition into Prediction Algorithms for Improved Surgical Risk Stratification”
  • Implications of mappings between ICD clinical diagnosis codes and Human Phenotype Ontology terms
  • Twitter
Powered byOpenScholar®Admin Login