Addictions

A Diffusion-Based Method for Learning the Multi-Outcome Distribution of Medical Treatments

In medicine, treatments often influence multiple, interdependent outcomes, such as primary endpoints, complications, adverse events, or other secondary endpoints. Hence, to make optimal treatment decisions, clinicians are interested in learning the distribution of multi-dimensional treatment outcomes. However, the vast majority of machine learning methods for predicting treatment effects focus o...

Chinese acupoint therapies in cancer pain management: research advances and future perspectives.

Cancer pain management (CPM) is crucial in oncology care, with current approaches including pharmacotherapy, radiotherapy, chemotherapy, nerve blocks, and psychological support. However, long-term drug use risks adverse effects and addiction, while physiotherapies often lack sustained efficacy. Therefore, identifying safer adjuvant analgesic therapies has become an urgent issue. Traditional Chines...

Predicting postoperative chronic opioid use with fair machine learning models integrating multi-modal data sources: a demonstration of ethical machine learning in healthcare.

OBJECTIVE: Building upon our previous work on predicting chronic opioid use using electronic health records (EHR) and wearable data, this study levera...

Automating the Addiction Behaviors Checklist for Problematic Opioid Use Identification.

IMPORTANCE: Individuals whose chronic pain is managed with opioids are at high risk of developing an opioid use disorder. Electronic health records (E...

Predictors of smartphone addiction in adolescents with depression: combing the machine learning and moderated mediation model approach.

Smartphone addiction (SA) significantly impacts the physical and mental health of adolescents, and can further exacerbate existing mental health issue...

Sustainable water allocation under climate change: Deep learning approaches to predict drinking water shortages.

Addressing sustainable urban water supply has become one of the most critical challenges for modern megacities, particularly in arid and semi-arid reg...

MythTriage: Scalable Detection of Opioid Use Disorder Myths on a Video-Sharing Platform

Understanding the prevalence of misinformation in health topics online can inform public health policies and interventions. However, measuring such ...

Spurious Correlations and Beyond: Understanding and Mitigating Shortcut Learning in SDOH Extraction with Large Language Models

Social determinants of health (SDOH) extraction from clinical text is critical for downstream healthcare analytics. Although large language models (...

Can Copulas Be Used for Feature Selection? A Machine Learning Study on Diabetes Risk Prediction

Accurate diabetes risk prediction relies on identifying key features from complex health datasets, but conventional methods like mutual information ...

Simulating Ethics: Using LLM Debate Panels to Model Deliberation on Medical Dilemmas

This paper introduces ADEPT, a system using Large Language Model (LLM) personas to simulate multi-perspective ethical debates. ADEPT assembles panel...

Sarcopenia and frailty: An in-depth analysis of the pathophysiology and effect on liver transplant candidates.

Cirrhosis represents the end stage of chronic liver disease, significantly reducing life expectancy as it progresses from a compensated to a decompens...

Navigating loss manifolds via rigid body dynamics: A promising avenue for robustness and generalisation

Training large neural networks through gradient-based optimization requires navigating high-dimensional loss landscapes, which often exhibit patholo...

AI-Augmented LLMs Achieve Therapist-Level Responses in Motivational Interviewing

Large language models (LLMs) like GPT-4 show potential for scaling motivational interviewing (MI) in addiction care, but require systematic evaluati...

Facial emotion based smartphone addiction detection and prevention using deep learning and video based learning.

Smartphone addiction among students has emerged as a critical issue, negatively impacting their academic performance, emotional well-being, and social...

Activation of the Carotid Body by Kappa Opioid Receptors Mitigates Fentanyl-Induced Respiratory Depression.

Previous studies reported that opioids depress breathing by inhibiting respiratory neural networks in the brainstem. The effects of opioids on sensory...

BoostDILI: Extreme Gradient Boost-Powered Drug-Induced Liver Injury Prediction and Structural Alerts Generation.

Over the past 60 years, drug-induced liver injury (DILI) has played a key role in the withdrawal of marketed drugs due to safety concerns. Early predi...

A Cautionary Tale on Integrating Studies with Disparate Outcome Measures for Causal Inference

Data integration approaches are increasingly used to enhance the efficiency and generalizability of studies. However, a key limitation of these meth...

Classification of internet addiction using machine learning on electroencephalography synchronization and functional connectivity.

BACKGROUND: Internet addiction (IA) refers to excessive internet use that causes cognitive impairment or distress. Understanding the neurophysiologica...

Sociodemographic Profile of People with Diagnosed Pancreatic Cancer in the UK: Retrospective Sentinel Network Cohort Study.

Pancreatic cancer is a devasting disease which is an increasing cause of cancer mortality. The aim of this study was to characterise, using descriptiv...

Neural Networks and Chemical Messengers: Insights into Tobacco Addiction.

This study investigates changes in resting-state networks (RSNs) associated with tobacco addiction (TA) and whether these changes reflect alterations ...