A systematic review of machine learning applications in predicting opioid associated adverse events

CR Ramírez Medina, J Benitez-Aurioles… - npj Digital …, 2025 - nature.com
Abstract Machine learning has increasingly been applied to predict opioid-related harms
due to its ability to handle complex interactions and generating actionable predictions. This …

Predictive Models to Assess Risk of Persistent Opioid Use, Opioid Use Disorder, and Overdose

SL Song, HG Dandapani, RS Estrada… - Journal of Addiction …, 2024 - journals.lww.com
Background This systematic review summarizes the development, accuracy, quality, and
clinical utility of predictive models to assess the risk of opioid use disorder (OUD), persistent …

Comparative performance of ChatGPT 3.5 and GPT4 on rhinology standardized board examination questions

EA Patel, L Fleischer, P Filip, M Eggerstedt, M Hutz… - OTO …, 2024 - Wiley Online Library
Objective Advances in deep learning and artificial intelligence (AI) have led to the
emergence of large language models (LLM) like ChatGPT from OpenAI. The study aimed to …

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

F Nateghi Haredasht, S Fouladvand, S Tate… - …, 2024 - Wiley Online Library
Background and aims Opioid use disorder (OUD) and opioid dependence lead to significant
morbidity and mortality, yet treatment retention, crucial for the effectiveness of medications …

[引用][C] The ChatGPT therapist will see you now: Navigating generative artificial intelligence's potential in addiction medicine research and patient care

S Tate, S Fouladvand, JH Chen, CYA Chen - Addiction, 2023 - Wiley Online Library
Generative AI offers potential for enhancing addiction medicine research and practice by
analyzing medical literature, improving research efficiency, streamlining clinical workflows …