Addictions

Predicting incident cardiovascular disease among African-American adults: A deep learning approach to evaluate social determinants of health in the Jackson heart study.

The present study sought to leverage machine learning approaches to determine whether social determinants of health improve prediction of incident cardiovascular disease (CVD). Participants in the Jackson Heart study with no history of CVD at baseline were followed over a 10-year period to determine first CVD events (i.e., coronary heart disease, stroke, heart failure). Three modeling algorithms (...

Outcome prediction of methadone poisoning in the United States: implications of machine learning in the National Poison Data System (NPDS).

Methadone is an opioid receptor agonist with a high potential for abuse. The current study aimed to compare different machine learning models to predict the outcomes following methadone poisoning. This six-year retrospective longitudinal study utilizes National Poison Data System (NPDS) data. The severity of outcomes was derived from the NPDS Coding Manual. Our database was divided into training (...

Patterns of Simultaneous and non-Simultaneous Use of Cannabis and Alcohol Among American Indian Adolescents.

BACKGROUND: American Indian (AI) adolescents report earlier initiation and higher rates of cannabis and alcohol use compared to their non-AI peers. Si...

Family ties: examining family functioning and alcohol use among American Indian youth.

PURPOSE: This study aims to examine a second-order latent variable of family functioning built from two established protective factors for American In...

The Promises and Possibilities of Artificial Intelligence in the Delivery of Behavior Analytic Services.

Artificial intelligence (AI) has begun to affect nearly every aspect of our daily lives and nearly every industry and profession. Many readers of this...

Ethical Considerations in Neuroprognostication Following Acute Brain Injury.

Neuroprognostication following acute brain injury (ABI) is a complex process that involves integrating vast amounts of information to predict a patien...

Uncertainty Quantification in Estimating Blood Alcohol Concentration From Transdermal Alcohol Level With Physics-Informed Neural Networks.

We develop an approach to estimate a blood alcohol signal from a transdermal alcohol signal using physics-informed neural networks (PINNs). Specifical...

Predicting Persistent Opioid Use after Hand Surgery: A Machine Learning Approach.

BACKGROUND: The aim of this study was to evaluate the use of machine learning to predict persistent opioid use after hand surgery.

Detecting Social Contexts from Mobile Sensing Indicators in Virtual Interactions with Socially Anxious Individuals.

Mobile sensing is a ubiquitous and useful tool to make inferences about individuals' mental health based on physiology and behavior patterns. Along wi...

Machine Learning Algorithms Predict Long-Term Postoperative Opioid Misuse: A Systematic Review.

INTRODUCTION: A steadily rising opioid pandemic has left the US suffering significant social, economic, and health crises. Machine learning (ML) domai...

Patterns of drinking behavior around a treatment episode for alcohol use disorder: Predictions from pre-treatment measures.

BACKGROUND: Alcohol use disorder (AUD) has been described as a chronic disease given the high rates that affected individuals have in returning to dri...

Enhanced phenotypes for identifying opioid overdose in emergency department visit electronic health record data.

BACKGROUND: Accurate identification of opioid overdose (OOD) cases in electronic healthcare record (EHR) data is an important element in surveillance,...

Design and Evaluation of Deep Learning Models for Continuous Acute Pain Detection Based on Phasic Electrodermal Activity.

The current method for assessing pain in clinical practice is subjective and relies on self-reported scales. An objective and accurate method of pain ...

A Novel Deep Learning Algorithm for Human Papillomavirus Infection Prediction in Head and Neck Cancers Using Routine Histology Images.

The etiology of head and neck squamous cell carcinoma (HNSCC) involves multiple carcinogens, such as alcohol, tobacco, and infection with human papill...

Multiobjective Molecular Optimization for Opioid Use Disorder Treatment Using Generative Network Complex.

Opioid use disorder (OUD) has emerged as a significant global public health issue, necessitating the discovery of new medications. In this study, we p...

Leading consumption patterns of psychoactive substances in Colombia: A deep neural network-based clustering-oriented embedding approach.

The number of health-related incidents caused using illegal and legal psychoactive substances (PAS) has dramatically increased over two decades worldw...

Artificial intelligence may help decision-making for withdrawal of life supporting treatment among the injured elderly.

De Novo Design of κ-Opioid Receptor Antagonists Using a Generative Deep-Learning Framework.

Likely effective pharmacological interventions for the treatment of opioid addiction include attempts to attenuate brain reward deficits during period...

Silk Fibroin Particles as Carriers in the Development of Hemoglobin-Based Oxygen Carriers.

Oxygen therapeutics have a range of applications in transfusion medicine and disease treatment. Synthetic molecules and all-natural or semi-synthetic ...

External validation of machine learning algorithm predicting prolonged opioid prescriptions in opioid-naïve lumbar spine surgery patients using a Taiwanese cohort.

BACKGROUND/PURPOSE: Identifying patients at risk of prolonged opioid use after surgery prompts appropriate prescription and personalized treatment pla...