Addictions

Low-burden data-driven work addiction screening: Comparing demographic and psychosocial predictors using machine learning.

BACKGROUND AND AIMS: Identifying employees at risk for work addiction is critical for early intervention, yet systematic screening approaches face practical challenges for organizations. This study compared two screening strategies: a demographic-only model that requires no additional data collection versus a psychosocial model that incorporates job satisfaction and job stress assessments. METHODS...

Noninvasive and minimally invasive approaches for acute and chronic stress assessment in dairy cattle.

In modern dairy production, cattle are routinely exposed to a wide range of management-related, environmental, and biological stressors all of which can compromise welfare, reproductive efficiency, immunocompetence, and lactation performance when the animal's adaptive capacity is exceeded. Accurate and timely stress quantification is therefore essential for welfare monitoring, research design, and...

From molecular mechanisms to digital surveillance: the 2010-2025 evolution of benzodiazepine and Z-drug misuse research.

The misuse of benzodiazepines (BZDs) and Z-drugs poses significant global public health challenges. This study maps the scientific evolution and parad...

Oxygen 1s photoelectron spectroscopy of gaseous alcohols and the σ-inductive properties of alkyl groups.

This study presents a comprehensive investigation of alcohol molecules in the gas phase using oxygen 1s X-ray photoelectron spectroscopy (O1s XPS), co...

Digital Conversation Companions.

Advances in voice recognition and artificial intelligence (AI) could soon yield digital conversation companions-software that combines conversational ...

Cognitive gains and addictive losses: The double-edged sword of Generative AI on employee creative thinking.

Drawing on Social Cognitive Theory (SCT), this study examines how Generative Artificial Intelligence (GAI) use is associated with employee creative th...

The Addition of Computer-Aided Detection (CADe) to Texture and Color Enhancement Imaging (TXI) Does Not Improve Adenoma Detection Rate During Colonoscopy: A Prospective Study.

OBJECTIVES: Early detection and resection of colorectal polyps prevent their progression toward advanced adenocarcinomas. The use of Texture and Color...

From Assistance to Dependence: The Cognitive Cost of Artificial Intelligence in Education.

BACKGROUND: The integration of Artificial Intelligence (AI) in education is reshaping how students interact with academic content, offering personaliz...

Reinventing Density Functional Theory with Machine Learning on Integral Features.

Kohn-Sham density functional theory remains central to electronic structure modeling, because of its efficiency and the steady gains in accuracy that ...

A machine learning-based screening tool for classifying post-traumatic stress disorder in firefighters: Firefighter Latent Risk Evaluation (FLARE).

BACKGROUND: The prevalence of post-traumatic stress disorder (PTSD) among South Korean firefighters is likely to be under-reported because of stigma a...

Behavioral patterns in iGaming across territories: Psychiatric and AI-driven insights via the internet of behavior.

BackgroundIn the digital era, iGaming has become a rapidly expanding phenomenon that generates complex behavioral patterns and increases the risk of p...

FEDI-CODE: A federated and causally-informed framework for dementia risk prediction using multi-site patient data.

Early detection of dementia is critical for timely intervention and disease management, yet it remains a challenging task due to the fragmented nature...

Embodied movement markers of alliance ruptures in psychotherapy: A machine learning study.

OBJECTIVE: Alliance ruptures are central to psychotherapy process and outcome, yet therapists often fail to detect them in real time, particularly wit...

Development and psychometric analysis of 5-item Artificial Intelligence Attitude (AIAT-5) for students.

In the context of accelerating integration of artificial intelligence (AI) technologies into education, rapidly assessing students' AI attitude is cru...

A machine learning framework for multimodal temporal prediction of neurological outcome after out-of-hospital cardiac arrest.

Neurological prognostication after out-of-hospital cardiac arrest (OHCA) remains challenging. Existing clinical scores rely on static, single-timepoin...

Predicting persistent pain after total knee arthroplasty using different machine learning algorithms.

BACKGROUND AND PURPOSE:  After total knee arthroplasty (TKA), 10-20% of patients remain unsatisfied. Well-performing clinical prediction models can pr...

Development of Data-Driven Models for Just-in-Time Digital Self-Management Advice to Improve Physical Functioning in Hip and Knee Osteoarthritis: Protocol for the e-cOAch Cross-Over Study.

BACKGROUND: Clinical guidelines recommend a stepped-care strategy for patients with hip and knee osteoarthritis that begins with nonoperative approach...

Evaluating machine learning algorithms at predicting developmental trajectories using sequential dataset truncation of voluntary alcohol consumption in adolescent mice.

BACKGROUND: Adolescent alcohol consumption is a known risk factor for developing alcohol use disorder (AUD) in adulthood, but individual susceptibilit...

Nanoelectronic Detection of Opioids: Machine Learning-Powered Screening With Carbon Nanotube Field-Effect Transistor Sensor Array.

The development of inexpensive, portable, and rapid opioid screening techniques is urgently needed because of the ongoing opioid crisis in the United ...

Integrating Psychosocial Factors into Artificial Intelligence Models for Predicting Addiction Treatment Outcomes: A Systematic Review.

INTRODUCTION: The integration of artificial intelligence (AI) into addiction research has expanded rapidly, yet it remains unclear how psychosocial, b...