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Characterize Disease Progression Subphenotypes in Real World Populations with Overweight and Obesity using a Graph-based Neural Network Framework

Obesity is a chronic, heterogeneous condition, with risks, trajectories, and treatment responses that vary widely among individuals. However, research characterizing the heterogeneity of long-term obesity progression—and its impact on the development of obesity-associated outcomes and treatment responses—is scarce. We aimed to identify progression subphenotypes in a real-world population with over...

Comparing computable structured phenotype- versus large language model-identification of opioid use disorder using electronic health record data

Opioid use disorder (OUD) is common in emergency departments (EDs); identification via structured computable phenotypes may miss important clinical context. Compare a computable structured OUD phenotype with a zero-shot large language model (LLM) using expert review as the reference. We retrospectively analyzed 202 adult ED encounters. Two emergency physicians independently determined OUD status w...

Machine learning-driven prediction of opioid and stimulant-related drug overdose fatalities: Analysis of the potential fourth wave

Between 2010 and 2021, fentanyl and stimulants co-involved deaths increased from 0.6% to 32.3% of all overdose deaths in the U.S. The Centers for Dise...

Heterogeneous epigenetic variation converges on splicing dysregulation in opioid addiction

Disease heterogeneity presents a major challenge for genetic and epigenetic dissection of complex traits. Neuropsychiatric traits, such as opioid use ...

Implementation of an Opioid Use Disorder (OUD) Machine-Learning Phenotype in Real-Time for the ADAPT Project

Develop and deploy a real-time, EHR-integrated machine learning phenotype to identify emergency department (ED) patients with opioid use disorder (OUD...

Machine learning augmented genome-wide meta-analysis of prescription opioid use in 860,000 individuals

Opioid analgesics are widely prescribed for pain, yet individuals vary markedly in their patterns of medical opioid use, influencing the risk of prolo...

Identifying high-dose opioid prescription risks using machine learning: A focus on sociodemographic characteristics.

OBJECTIVE: The objective of this study was to leverage machine learning techniques to analyze administrative claims and socioeconomic data, with the a...

Functional Brain Network Identification in Opioid Use Disorder Using Machine Learning Analysis of Resting-State fMRI BOLD Signals

Understanding the neurobiology of opioid use disorder (OUD) using resting-state functional magnetic resonance imaging (rs-fMRI) may help inform trea...

A Thematic Framework for Analyzing Large-scale Self-reported Social Media Data on Opioid Use Disorder Treatment Using Buprenorphine Product

Background: One of the key FDA-approved medications for Opioid Use Disorder (OUD) is buprenorphine. Despite its popularity, individuals often report...

Annotation of Opioid Use Disorder Entity Modifiers in Clinical Text.

Natural Language Processing can be used to identify opioid use disorder in patients from clinical text1. We annotate a corpus of clinical text for men...

Erector Spinae Plane Block versus Transversus Abdominis Plane Block for Robotic Inguinal Hernia Repair: A Blinded, Active-Controlled, Randomized Trial.

BACKGROUND: Regional anesthetic nerve blocks are widely used in the treatment of pain after outpatient surgery to reduce opioid consumption. Erector s...

Identification of Subphenotypes of Opioid Use Disorder Using Unsupervised Machine Learning.

This paper aimed to detect the latent clusters of patients with opioid use disorder and to identify the risk factors affecting drug misuse using unsup...

Using Natural Language Processing of Clinical Notes to Predict Outcomes of Opioid Treatment Program.

Potential of natural language processing (NLP) in extracting patient's information from clinical notes of opioid treatment programs (OTP) and leveragi...

Editorial Commentary: Big Data and Machine Learning in Medicine.

Recent research using machine learning and data mining to determine predictors of prolonged opioid use after arthroscopic surgery showed that Artifici...

Bias and fairness assessment of a natural language processing opioid misuse classifier: detection and mitigation of electronic health record data disadvantages across racial subgroups.

OBJECTIVES: To assess fairness and bias of a previously validated machine learning opioid misuse classifier.

Comment on: "Predictive factors for opioid-free management after robotic radical prostatectomy: the value of a single-port robotic platform".

Extraperitoneal Single-Port Robot-Assisted Radical Prostatectomy.

Robot-assisted radical prostatectomy (RARP) is currently the standard minimally invasive procedure for the surgical management of localized prostate c...

Prediction of Prolonged Opioid Use After Surgery in Adolescents: Insights From Machine Learning.

BACKGROUND: Long-term opioid use has negative health care consequences. Patients who undergo surgery are at risk for prolonged opioid use after surger...

Identifying risk of opioid use disorder for patients taking opioid medications with deep learning.

OBJECTIVE: The United States is experiencing an opioid epidemic. In recent years, there were more than 10 million opioid misusers aged 12 years or old...

Clinical Performance of a Gene-Based Machine Learning Classifier in Assessing Risk of Developing OUD in Subjects Taking Oral Opioids: A Prospective Observational Study.

OBJECTIVE: To reduce the incidence of Opioid Use Disorder (OUD), multiple guidelines recommend assessing the risk of OUD prior to prescribing oral opi...