AIMC Topic: Analgesics, Opioid
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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...
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 ...
Machine Learned Classification of Ligand Intrinsic Activities at Human μ-Opioid Receptor.
ACS chemical neuroscience
Jul 11, 2024
Opioids are small-molecule agonists of μ-opioid receptor (μOR), while reversal agents such as naloxone are antagonists of μOR. Here, we developed machine learning (ML) models to classify the intrinsic activities of ligands at the human μOR based on t...
Harnessing artificial intelligence for predicting and managing postoperative pain: a narrative literature review.
Current opinion in anaesthesiology
Jun 25, 2024
PURPOSE OF REVIEW: This review examines recent research on artificial intelligence focusing on machine learning (ML) models for predicting postoperative pain outcomes. We also identify technical, ethical, and practical hurdles that demand continued i...
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.
Opioid Nonadherence Risk Prediction of Patients with Cancer-Related Pain Based on Five Machine Learning Algorithms.
Pain research & management
Jun 6, 2024
OBJECTIVES: Opioid nonadherence represents a significant barrier to cancer pain treatment efficacy. However, there is currently no effective prediction method for opioid adherence in patients with cancer pain. We aimed to develop and validate a machi...
Deep learning predicts postoperative opioids refills in a multi-institutional cohort of surgical patients.
Surgery
May 25, 2024
BACKGROUND: To combat the opioid epidemic, several strategies were implemented to limit the unnecessary prescription of opioids in the postoperative period. However, this leaves a subset of patients who genuinely require additional opioids with inade...
Prediction of naloxone dose in opioids toxicity based on machine learning techniques (artificial intelligence).
Daru : journal of Faculty of Pharmacy, Tehran University of Medical Sciences
May 21, 2024
BACKGROUND: Treatment management for opioid poisoning is critical and, at the same time, requires specialized knowledge and skills. This study was designed to develop and evaluate machine learning algorithms for predicting the maintenance dose and du...
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