AIMC Topic: Opioid-Related Disorders
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A neural network approach to predict opioid misuse among previously hospitalized patients using electronic health records.
PloS one
Aug 28, 2024
Can Electronic Health Records (EHR) predict opioid misuse in general patient populations? This research trained three backpropagation neural networks to explore EHR predictors using existing patient data. Model 1 used patient diagnosis codes and was ...
Development and validation of machine-learning algorithms predicting retention, overdoses, and all-cause mortality among US military veterans treated with buprenorphine for opioid use disorder.
Journal of addictive diseases
Jun 30, 2024
BACKGROUND: Buprenorphine for opioid use disorder (B-MOUD) is essential to improving patient outcomes; however, retention is essential.
Self-help groups and opioid use disorder treatment: An investigation using a machine learning-assisted robust causal inference framework.
International journal of medical informatics
Jun 24, 2024
OBJECTIVES: This study investigates the impact of participation in self-help groups on treatment completion among individuals undergoing medication for opioid use disorder (MOUD) treatment. Given the suboptimal adherence and retention rates for MOUD,...
Predictability of buprenorphine-naloxone treatment retention: A multi-site analysis combining electronic health records and machine learning.
Addiction (Abingdon, England)
Jun 24, 2024
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 like buprenorphine-naloxone, remains unpredictable. Our objective ...
Prediction of sustained opioid use in children and adolescents using machine learning.
British journal of anaesthesia
Jun 10, 2024
BACKGROUND: Opioid misuse in the paediatric population is understudied. This study aimed to develop a machine learning classifier to differentiate between occasional and sustained opioid users among children and adolescents in outpatient settings.
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...
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