AIMC Topic: Opioid-Related Disorders
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Spatial patterns of rural opioid-related hospital emergency department visits: A machine learning analysis.
Health & place
Oct 13, 2024
As opioid-related overdose emergency department visits continue to rise in the United States, there is a need to understand the location and magnitude of the crisis, especially in at-risk rural areas. We analyzed sets of ZIP code level electronic hea...
Factors predicting access to medications for opioid use disorder for housed and unhoused patients: A machine learning approach.
PloS one
Sep 27, 2024
BACKGROUND: Opioid use disorder (OUD) is a growing public health crisis, with opioids involved in an overwhelming majority of drug overdose deaths in the United States in recent years. While medications for opioid use disorder (MOUD) effectively redu...
The Use of Natural Language Processing Methods in Reddit to Investigate Opioid Use: Scoping Review.
JMIR infodemiology
Sep 13, 2024
BACKGROUND: The growing availability of big data spontaneously generated by social media platforms allows us to leverage natural language processing (NLP) methods as valuable tools to understand the opioid crisis.
Classifying High-Risk Patients for Persistent Opioid Use After Major Spine Surgery: A Machine-Learning Approach.
Anesthesia and analgesia
Sep 4, 2024
BACKGROUND: Persistent opioid use is a common occurrence after surgery and prolonged exposure to opioids may result in escalation and dependence. The objective of this study was to develop machine-learning-based predictive models for persistent opioi...
AI and Big Data approaches to addressing the opioid crisis: a scoping review protocol.
BMJ open
Aug 31, 2024
INTRODUCTION: This paper outlines the steps necessary to assess the latest developments in artificial intelligence (AI) as well as Big Data technologies and their relevance to the opioid crisis. Fatal opioid overdoses have risen to over 82 998 annual...
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.
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