AIMC Topic: Opioid-Related Disorders
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Machine Learning-Driven Analysis of Individualized Treatment Effects Comparing Buprenorphine and Naltrexone in Opioid Use Disorder Relapse Prevention.
Journal of addiction medicine
May 22, 2024
OBJECTIVE: A trial comparing extended-release naltrexone and sublingual buprenorphine-naloxone demonstrated higher relapse rates in individuals randomized to extended-release naltrexone. The effectiveness of treatment might vary based on patient char...
Machine learning identifies risk factors associated with long-term opioid use in fibromyalgia patients newly initiated on an opioid.
RMD open
May 20, 2024
OBJECTIVES: Fibromyalgia is frequently treated with opioids due to limited therapeutic options. Long-term opioid use is associated with several adverse outcomes. Identifying factors associated with long-term opioid use is the first step in developing...
An explainable machine learning framework for predicting the risk of buprenorphine treatment discontinuation for opioid use disorder among commercially insured individuals.
Computers in biology and medicine
Apr 22, 2024
OBJECTIVES: Buprenorphine is an effective evidence-based medication for opioid use disorder (OUD). Yet premature discontinuation undermines treatment effectiveness, increasing the risk of mortality and overdose. We developed and evaluated a machine l...
Predicting Persistent Opioid Use after Hand Surgery: A Machine Learning Approach.
Plastic and reconstructive surgery
Sep 29, 2023
BACKGROUND: The aim of this study was to evaluate the use of machine learning to predict persistent opioid use after hand surgery.
Machine Learning Algorithms Predict Long-Term Postoperative Opioid Misuse: A Systematic Review.
The American surgeon
Sep 21, 2023
INTRODUCTION: A steadily rising opioid pandemic has left the US suffering significant social, economic, and health crises. Machine learning (ML) domains have been utilized to predict prolonged postoperative opioid (PPO) use. This systematic review ai...
Multiobjective Molecular Optimization for Opioid Use Disorder Treatment Using Generative Network Complex.
Journal of medicinal chemistry
Aug 25, 2023
Opioid use disorder (OUD) has emerged as a significant global public health issue, necessitating the discovery of new medications. In this study, we propose a deep generative model that combines a stochastic differential equation (SDE)-based diffusio...
De Novo Design of κ-Opioid Receptor Antagonists Using a Generative Deep-Learning Framework.
Journal of chemical information and modeling
Aug 9, 2023
Likely effective pharmacological interventions for the treatment of opioid addiction include attempts to attenuate brain reward deficits during periods of abstinence. Pharmacological blockade of the κ-opioid receptor (KOR) has been shown to abolish b...
A deep learning method to detect opioid prescription and opioid use disorder from electronic health records.
International journal of medical informatics
Dec 31, 2022
OBJECTIVE: As the opioid epidemic continues across the United States, methods are needed to accurately and quickly identify patients at risk for opioid use disorder (OUD). The purpose of this study is to develop two predictive algorithms: one to pred...
Using natural language processing to identify opioid use disorder in electronic health record data.
International journal of medical informatics
Dec 10, 2022
BACKGROUND: As opioid prescriptions have risen, there has also been an increase in opioid use disorder (OUD) and its adverse outcomes. Accurate and complete epidemiologic surveillance of OUD, to inform prevention strategies, presents challenges. The ...
Classifying Characteristics of Opioid Use Disorder From Hospital Discharge Summaries Using Natural Language Processing.
Frontiers in public health
May 9, 2022
BACKGROUND: Opioid use disorder (OUD) is underdiagnosed in health system settings, limiting research on OUD using electronic health records (EHRs). Medical encounter notes can enrich structured EHR data with documented signs and symptoms of OUD and s...
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