Addictions

Enhancing Anterior Quadratus Lumborum Block Accuracy with Artificial Intelligence: A Segmentation Approach Evaluated by Dice Score Metrics

Anterior quadratus lumborum (QL) block is a regional anesthesia technique shown to provide both somatic and visceral pain relief by targeting lower thoracic nerves and the thoracic sympathetic trunk. Despite its clinical benefits, success depends on accurate sonoanatomic identification, which can be challenging due to individual anatomical variations. In this study, we developed an artificial inte...

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 co...

Deceptive Bias Measurement in Deep Learning: Assessing Shortcut Reliance in TCGA Cancer Models

Bias in machine learning is a persistent challenge because it can create unfair outcomes, limit generalization, and reduce trust in real-world applica...

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...

An integrated analytical framework for gender-based violence research: A simulation study combining machine learning and causal inference

Current research on Gender-Based Violence (GBV) typically separates predictive machine learning and causal inference into distinct analytical silos. Y...

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...

Mapping research on ICT addiction: a comprehensive review of Internet, smartphone, social media, and gaming addictions.

INTRODUCTION: The use of information and communication technologies such as the Internet, smartphones, social media, and gaming has gained significant...

Effectiveness of digital self-care device for at risk drinking problems: focus on individuals at risk for alcohol-related issues.

AIMS: This study was conducted to verify the effectiveness of using digital self-care devices in reducing alcohol-related problems among high-risk alc...

The Hypothalamic Medial Preoptic Area-Paraventricular Nucleus Circuit Modulates Depressive-Like Behaviors in a Mouse Model of Postpartum Depression.

Estrogen fluctuations have been implicated in various mood disorders, including perimenopausal and postpartum depression (PPD), likely through complex...

Cytopathological quantification of NORs using artificial intelligence to oral cancer screening.

Oral squamous cell carcinoma (OSCC) remains the most prevalent neoplasm of the head and neck. In recent decades, the incidence and prevalence of OSCC ...

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...

Development of machine learning-based quantitative structure-activity relationship models for predicting plasma half-lives of drugs in six common food animal species.

Plasma half-life is a crucial pharmacokinetic parameter for estimating extralabel withdrawal intervals of drugs to ensure the safety of food products ...

Revealing the Self: Brainwave-Based Human Trait Identification

People exhibit unique emotional responses. In the same scenario, the emotional reactions of two individuals can be either similar or vastly differen...

Performance evaluation of mixed-precision Runge-Kutta methods for the solution of partial differential equations

This work focuses on the numerical study of a recently published class of Runge-Kutta methods designed for mixed-precision arithmetic. We employ the...

Using Instruction-Tuned Large Language Models to Identify Indicators of Vulnerability in Police Incident Narratives

Objectives: Compare qualitative coding of instruction tuned large language models (IT-LLMs) against human coders in classifying the presence or abse...

A Panopticon on My Wrist: The Biopower of Big Data Visualization for Wearables

Big data visualization - the visual-spatial display of quantitative information culled from huge data sets - is now firmly embedded within the every...

Utilizing RAG and GPT-4 for Extraction of Substance Use Information from Clinical Notes.

This research investigates the application of a hybrid Retrieval-Augmented Generation (RAG) and Generative Pre-trained Transformer (GPT) pipeline for ...