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Predictability of buprenorphine-naloxone treatment retention: A multi-site analysis combining electronic health records and machine learning.

Fateme Nateghi HaredashtSajjad FouladvandSteven TateMin Min ChanJoannas Jie Lin YeowKira GriffithsIvan LopezJeremiah W BertzAdam S MinerTina Hernandez-BoussardChwen-Yuen Angie ChenHuiqiong DengKeith N HumphreysAnna LembkeL Alexander VanceJonathan H Chen
Published in: Addiction (Abingdon, England) (2024)
US patients with opioid use disorder or opioid dependence treated with buprenorphine-naloxone prescriptions appear to have a high (∼60%) treatment attrition by 6 months. Machine learning models trained on diverse electronic health record datasets appear to be able to predict treatment continuity with accuracy comparable to that of clinical experts.
Keyphrases
  • electronic health record
  • machine learning
  • clinical decision support
  • chronic pain
  • adverse drug
  • rna seq
  • newly diagnosed
  • high intensity
  • data analysis