AIMC Topic: Opioid-Related Disorders

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Explainable machine learning to predict prolonged post-operative opioid use in rotator cuff patients.

BMC musculoskeletal disorders
BACKGROUND: Opioid overuse is a costly and significant problem in the United States. Medical specialties including surgery are a contributor to opioid prescriptions while having few clear prescribing guidelines. Machine learning predictive tools can ...

Monitoring Opioid-Related Social Media Chatter Using Natural Language Processing and Large Language Models: Temporal Analysis.

JMIR infodemiology
BACKGROUND: Opioid overdose is a global public health emergency, with the United States experiencing high rates of morbidity and mortality due to prescription and illicit opioid use. Traditional public health monitoring systems often fail to provide ...

Relative importance of socioecological domains to predicting opioid-involved mortality.

PloS one
BACKGROUND: The opioid crisis in the United States is a complex issue with interconnected factors that lead to opioid misuse and opioid-involved mortality. This study assessed the relative importance of different risk factor domains in predicting fat...

Self-Disclosure and Social Support in a Web-Based Opioid Recovery Community: Machine Learning Analysis.

JMIR formative research
BACKGROUND: The opioid crisis remains a critical public health challenge, with opioid use disorder (OUD) imposing significant societal and health care burdens. Web-based communities, such as the Reddit community r/OpiatesRecovery, provide an anonymou...

Mitigating Opioid Dependence in Orthopaedic Surgery: Current Strategies and Future Directions.

British journal of hospital medicine (London, England : 2005)
The opioid crisis presents a significant burden to patients and healthcare systems. Orthopaedic surgery involves treating patients with significant pain demands, therefore opioid stewardship in this specialty is an important area in targeting the opi...

Sentiment Analysis Using a Large Language Model-Based Approach to Detect Opioids Mixed With Other Substances Via Social Media: Method Development and Validation.

JMIR infodemiology
BACKGROUND: The opioid crisis poses a significant health challenge in the United States, with increasing overdoses and death rates due to opioids mixed with other illicit substances. Various strategies have been developed by federal and local governm...

Identifying emergency department patients at high risk for opioid overdose using natural language processing and machine learning.

Journal of substance use and addiction treatment
INTRODUCTION: Emergency departments (ED) are potential sites for identifying and treating individuals at high risk for opioid overdose. This study used machine learning (ML)-based models to predict opioid overdose death in the 12 months after an ED v...

Development and evaluation of a machine learning model to predict acute care for opioid use disorder among Medicaid enrollees engaged in a community-based treatment program.

Addiction (Abingdon, England)
AIMS: To develop machine-learning algorithms for predicting the risk of a hospitalization or emergency department (ED) visit for opioid use disorder (OUD) (i.e. OUD acute events) in Pennsylvania Medicaid enrollees in the Opioid Use Disorder Centers o...

Are Treatment Services Ready for the Use of Big Data Analytics and AI in Managing Opioid Use Disorder?

Journal of medical Internet research
In this viewpoint, we explore the use of big data analytics and artificial intelligence (AI) and discuss important challenges to their ethical, effective, and equitable use within opioid use disorder (OUD) treatment settings. Applying our collective ...