Objectives To assess fairness and bias of a previously validated machine learning opioid
misuse classifier. Materials & Methods Two experiments were conducted with the classifier's …

This systematic review and meta‐analysis seek to identify prevalent machine learning (ML)
models applied to outcomes related to illicit opioid use. Following PRISMA guidelines, we …

Background Nonalcoholic fatty liver disease (NAFLD) poses a substantial socioeconomic
burden and is becoming the fastest growing driver of chronic liver disease, potentially …

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 …

This study aims to train and validate machine learning and deep learning models to identify
patients with risky alcohol and drug misuse in a Screening, Brief Intervention, and Referral to …

Objective Opioid use disorder (OUD) is a chronic relapsing disorder with a problematic
pattern of opioid use, affecting nearly 27 million people worldwide. Machine learning (ML) …

Background Improving the identification of people who inject drugs (PWID) in electronic
medical records can improve clinical decision making, risk assessment and mitigation, and …

Opioid use disorder is known to be under-coded as a diagnosis, yet problematic opioid use
can be documented in clinical notes, which are included in electronic health records. We …

Background Despite restrictive opioid management guidelines, opioid use disorder (OUD)
remains a major public health concern. Machine learning (ML) offers a promising avenue for …

Abstract Background and Aims Accurate case discovery is critical for disease surveillance,
resource allocation and research. International Classification of Disease (ICD) diagnosis …