Addictions

Integrating Artificial Intelligence Technology Into Simulation for Pre- and Postlicensure Nursing Students.

Advances in artificial intelligence (AI) technologies have not been widely integrated into simulation education. This work examines the process of designing and implementing AI-enabled opioid-involved overdose simulation scenarios to aid pre- and postlicensure nursing students in learning how to assess, respond to, and manage opioid-involved overdoses. Thirty students provided feedback on their en...

AI-powered topic modeling: comparing LDA and BERTopic in analyzing opioid-related cardiovascular risks in women.

Topic modeling is a crucial technique in natural language processing (NLP), enabling the extraction of latent themes from large text corpora. Traditional topic modeling, such as Latent Dirichlet Allocation (LDA), faces limitations in capturing the semantic relationships in the text document although it has been widely applied in text mining. BERTopic, created in 2022, leveraged advances in deep le...

Finding purpose: Integrated latent profile and machine learning analyses identify purpose in life as an important predictor of high-functioning recovery after alcohol treatment.

BACKGROUND: Recent investigations of recovery from alcohol use disorder (AUD) have distinguished subgroups of high and low functioning recovery in dat...

The Mechanism of Bisphenol S-Induced Atherosclerosis Elucidated Based on Network Toxicology, Molecular Docking, and Machine Learning.

The increasing prevalence of environmental pollutants has raised public concern about their potential role in diseases such as atherosclerosis (AS). E...

Explaining electroencephalogram channel and subband sensitivity for alcoholism detection.

Alcoholism, a progressive loss of control over alcohol consumption, deteriorates mental and physical health over time. Automatic alcoholism detection ...

Neural Network-Assisted Dual-Functional Hydrogel-Based Microfluidic SERS Sensing for Divisional Recognition of Multimolecule Fingerprint.

To enhance the sensitivity, integration, and practicality of the Raman detection system, a deep learning-based dual-functional subregional microfluidi...

Hybrid multi-modality multi-task learning for forecasting progression trajectories in subjective cognitive decline.

While numerous studies strive to exploit the complementary potential of MRI and PET using learning-based methods, the effective fusion of the two moda...

Triboelectric Sensors Based on Glycerol/PVA Hydrogel and Deep Learning Algorithms for Neck Movement Monitoring.

Prolonged use of digital devices and sedentary lifestyles have led to an increase in the prevalence of cervical spondylosis among young people, highli...

Predictive modeling of methadone poisoning outcomes in children ≤ 5 years: utilizing machine learning and the National Poison Data System for improved clinical decision-making.

UNLABELLED: The escalating therapeutic use of methadone has coincided with an increase in accidental ingestions, particularly among children ≤ 5 years...

Optimized machine learning approaches to combine surface-enhanced Raman scattering and infrared data for trace detection of xylazine in illicit opioids.

Infrared absorption spectroscopy and surface-enhanced Raman spectroscopy were integrated into three data fusion strategies-hybrid (concatenated spectr...

Biopsychosocial based machine learning models predict patient improvement after total knee arthroplasty.

Total knee arthroplasty (TKA) is an effective treatment for end stage osteoarthritis. However, biopsychosocial features are not routinely considered i...

A deep learning-based system for automatic detection of emesis with high accuracy in Suncus murinus.

Quantifying emesis in Suncus murinus (S. murinus) has traditionally relied on direct observation or reviewing recorded behaviour, which are laborious,...

Detecting Opioid Use Disorder in Health Claims Data With Positive Unlabeled Learning.

Accurate detection and prevalence estimation of behavioral health conditions, such as opioid use disorder (OUD), are crucial for identifying at-risk i...

Machine learning research methods to predict postoperative pain and opioid use: a narrative review.

The use of machine learning to predict postoperative pain and opioid use has likely been catalyzed by the availability of complex patient-level data, ...

Eye-tracking dataset of endoscopist-AI teaming during colonoscopy: Retrospective and real-time acquisition.

Recent studies have demonstrated that integrating AI into colonoscopy procedures significantly improves the adenoma detection rate (ADR) and reduces t...

A deep learning analysis for dual healthcare system users and risk of opioid use disorder.

The opioid crisis has disproportionately affected U.S. veterans, leading the Veterans Health Administration to implement opioid prescribing guidelines...

K-nearest neighbor algorithm for imputing missing longitudinal prenatal alcohol data.

AIMS: The objective of this study is to illustrate the application of a machine learning algorithm, K Nearest Neighbor () to impute missing alcohol da...

A Review of Leveraging Artificial Intelligence to Predict Persistent Postoperative Opioid Use and Opioid Use Disorder and its Ethical Considerations.

PURPOSE OF REVIEW: Artificial intelligence (AI) offers a new frontier for aiding in the management of both acute and chronic pain, which may potential...

Machine learning algorithms for predicting PTSD: a systematic review and meta-analysis.

This study aimed to compare and evaluate the prediction accuracy and risk of bias (ROB) of post-traumatic stress disorder (PTSD) predictive models. We...

A machine learning-based model for predicting the risk of cognitive frailty in elderly patients on maintenance hemodialysis.

Elderly patients undergoing maintenance hemodialysis (MHD) face a heightened risk of cognitive frailty (CF), which significantly compromises quality o...