AIMC Topic: Opioid-Related Disorders
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Artificial intelligence interventions focused on opioid use disorders: A review of the gray literature.
The American journal of drug and alcohol abuse
Oct 2, 2020
BACKGROUND: With the artificial intelligence (AI) paradigm shift comes momentum toward the development and scale-up of novel AI interventions to aid in opioid use disorder (OUD) care, in the identification of overdose risk, and in the reversal of ove...
Using machine learning to predict risk of incident opioid use disorder among fee-for-service Medicare beneficiaries: A prognostic study.
PloS one
Jul 17, 2020
OBJECTIVE: To develop and validate a machine-learning algorithm to improve prediction of incident OUD diagnosis among Medicare beneficiaries with ≥1 opioid prescriptions.
Publicly available machine learning models for identifying opioid misuse from the clinical notes of hospitalized patients.
BMC medical informatics and decision making
Apr 29, 2020
BACKGROUND: Automated de-identification methods for removing protected health information (PHI) from the source notes of the electronic health record (EHR) rely on building systems to recognize mentions of PHI in text, but they remain inadequate at e...
Machine Learning Based Opioid Overdose Prediction Using Electronic Health Records.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Mar 4, 2020
Opioid addiction in the United States has come to national attention as opioid overdose (OD) related deaths have risen at alarming rates. Combating opioid epidemic becomes a high priority for not only governments but also healthcare providers. This d...
Understanding Opioid Use Disorder (OUD) using tree-based classifiers.
Drug and alcohol dependence
Jan 15, 2020
BACKGROUND: Opioid Use Disorder (OUD), defined as a physical or psychological reliance on opioids, is a public health epidemic. Identifying adults likely to develop OUD can help public health officials in planning effective intervention strategies. T...
Rapid Assessment of Opioid Exposure and Treatment in Cities Through Robotic Collection and Chemical Analysis of Wastewater.
Journal of medical toxicology : official journal of the American College of Medical Toxicology
Jan 9, 2020
INTRODUCTION: Accurate data regarding opioid use, overdose, and treatment is important in guiding community efforts at combating the opioid epidemic. Wastewater-based epidemiology (WBE) is a potential method to quantify community-level trends of opio...
Female
Adult
Male
Middle Aged
Humans
Wastewater
Analgesics, Opioid
Opioid-Related Disorders
Solid Phase Extraction
North Carolina
Naloxone
Narcotic Antagonists
Opioid Epidemic
Water Pollutants, Chemical
Environmental Monitoring
Robotics
Urban Health
Substance-Related Disorders
Mass Spectrometry
Feasibility Studies
Using machine learning to predict opioid misuse among U.S. adolescents.
Preventive medicine
Nov 6, 2019
This study evaluated prediction performance of three different machine learning (ML) techniques in predicting opioid misuse among U.S. adolescents. Data were drawn from the 2015-2017 National Survey on Drug Use and Health (N = 41,579 adolescents, age...
Machine Learning and Natural Language Processing for Geolocation-Centric Monitoring and Characterization of Opioid-Related Social Media Chatter.
JAMA network open
Nov 1, 2019
IMPORTANCE: Automatic curation of consumer-generated, opioid-related social media big data may enable real-time monitoring of the opioid epidemic in the United States.
Computational Systems Pharmacology-Target Mapping for Fentanyl-Laced Cocaine Overdose.
ACS chemical neuroscience
Jul 15, 2019
The United States of America is fighting against one of its worst-ever drug crises. Over 900 people a week die from opioid- or heroin-related overdoses, while millions more suffer from opioid prescription addiction. Recently, drug overdoses caused by...
Using natural language processing of clinical text to enhance identification of opioid-related overdoses in electronic health records data.
Pharmacoepidemiology and drug safety
Jun 19, 2019
PURPOSE: To enhance automated methods for accurately identifying opioid-related overdoses and classifying types of overdose using electronic health record (EHR) databases.
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