Latest AI and machine learning research in addictions for healthcare professionals.
Alcohol use disorder (AUD) disrupts the gut-liver-brain axis, yet mechanistically grounded and therapeutically actionable targets within this network remain poorly defined. To identify microbial modulators of alcohol-induced tissue pathology, longitudinal advanced diffusion MRI and fecal 16S rRNA profiling were integrated across Marchigian Sardinian alcohol-preferring rats evaluated at baseline, a...
Feature selection is a critical step in electronic health record (EHR)-based predictive modeling, where input variables are often high-dimensional, sparse, noisy, and redundant. Large feature sets not only increase computational burden and overfitting risk, but also make model interpretation difficult, leading to limited usefulness in clinical settings. In this study, we focus on diagnosis-related...
Agentic vision-language models (VLMs), which interleave textual reasoning with explicit tool calls such as cropping and code-based image manipulation,...
Multimodal large language models increasingly use sketches, annotations, tools, and intermediate images during reasoning, but it remains unclear wheth...
High-dimensional data with sparse structure and spatio-temporal dependence arise in many scientific domains. We develop a Bayesian feature-extraction ...
Validated measures of pain catastrophizing primarily assess catastrophizing as a stable trait. However, emerging evidence suggests catastrophizing flu...
Generative artificial intelligence (AI) has emerged as a powerful framework for drug discovery, yet most current approaches follow one-drug-one-gene t...
This study focuses on the relationship between access to Advanced Neonatal Care (ANC) and fertility across the regions in Ghana between 1988 and 2022....
Deep learning models for prostate MRI-based cancer grading may encode clinical covariates that either reflect useful disease-related signal or non-gen...
Adolescent use of alcohol, nicotine, and marijuana remains a major public health concern in the United States. Early identification of youth at elevat...
Vision language models (VLMs) have made remarkable progress in visual reasoning during the last decade. Most evaluations have used simple scenes (MS-C...
Exogenous opioids that activate mu-opioid receptors (MORs) in nociceptive circuits mediate transient pain relief lasting minutes to hours but have mor...
Background: Postoperative delirium (POD) is a complication associated with most types of surgery, and is associated with a number of detrimental effec...
Road traffic accidents remain a critical global crisis, consistently serving as a primary driver of preventable mortality and severe injury. These inc...
Background Machine learning (ML) models are increasingly used to predict adverse outcomes after surgery. However, most rely on static patient characte...
Objective: Stigmatizing language in the electronic health record (EHR) has been associated with adverse patient experience in substance use disorder c...
Machine learning is increasingly applied to species-level biological data, but phylogenetic autocorrelation can make evaluation species statistically ...
Machine learning is accelerating biomedical research. Cross-validation is widely used to compare predictive performance -- not only to benchmark algor...
Human behavioral and mental health outcomes arise from interactions among genetic, environmental, and neurobiological systems. Existing frameworks oft...
Randomized neural networks (RdNNs) enable efficient, backpropagation-free training by freezing randomly initialized input-to-hidden weights, which per...