Smoking & Tobacco

Using machine learning to extract information and predict outcomes from reports of randomised trials of smoking cessation interventions in the Human Behaviour-Change Project.

BACKGROUND: Using reports of randomised trials of smoking cessation interventions as a test case, this study aimed to develop and evaluate machine learning (ML) algorithms for extracting information from study reports and predicting outcomes as part of the Human Behaviour-Change Project. It is the first of two linked papers, with the second paper reporting on further development of a prediction sy...

Explainable Deep Learning Approaches for Risk Screening of Periodontitis.

Several pieces of evidence have been reported regarding the association between periodontitis and systemic diseases. Despite the emphasized significance of prevention and early diagnosis of periodontitis, there is still a lack of a clinical tool for early screening of this condition. Therefore, this study aims to use explainable artificial intelligence (XAI) technology to facilitate early screenin...

The application of artificial intelligence in health communication development: A scoping review.

This scoping review explores the integration of Artificial Intelligence (AI) with communication, behavioral, and social theories to enhance health beh...

Identifying Topics Around Nicotine Gum: A Machine Learning Approach with Twitter Data.

BACKGROUND: Nicotine gum products from brands like Lucy and Rogue are relatively new arrivals to the tobacco marketplace. While studies of correlates ...

Regression prediction of tobacco chemical components during curing based on color quantification and machine learning.

Color is one of the most important indicators to characteristic the quality of tobacco, which is strongly related to the variations of chemical compon...

A first step towards a machine learning-based framework for bloodstain classification in forensic science.

Bloodstains found at a crime scene can help estimate the events that occurred during the crime. Reconstructing the crime scene by analyzing the bloods...

Identification of Biomarkers and Molecular Pathways Implicated in Smoking and COVID-19 Associated Lung Cancer Using Bioinformatics and Machine Learning Approaches.

Lung cancer (LC) is a significant global health issue, with smoking as the most common cause. Recent epidemiological studies have suggested that indiv...

A Comparison of Three Automated Root-Knot Nematode Egg Counting Approaches Using Machine Learning, Image Analysis, and a Hybrid Model.

spp. (root-knot nematodes [RKNs]) are a major threat to a wide range of agricultural crops worldwide. Breeding crops for RKN resistance is an effecti...

Mesocorticolimbic and Cardiometabolic Diseases-Two Faces of the Same Coin?

The risk behaviors underlying the most prevalent chronic noncommunicable diseases (NCDs) encompass alcohol misuse, unhealthy diets, smoking and sedent...

Using machine learning models to identify the risk of depression in middle-aged and older adults with frequent and infrequent nicotine use: A cross-sectional study.

BACKGROUND: Depression is very prevalent in middle-aged and older smokers. Therefore, we aimed to identify the risk of depression among middle-aged an...

Machine Learning-Based Prediction of Binge Drinking among Adults in the United State: Analysis of the 2022 Health Information National Trends Survey.

Little is known about the association of social media and belief in alcohol and cancer with binge drinking. This study aimed to perform feature select...

Epidemiological breast cancer prediction by country: A novel machine learning approach.

Breast cancer remains a significant contributor to cancer-related deaths among women globally. We seek for this study to examine the correlation betwe...

Predicting the Hallucinogenic Potential of Molecules Using Artificial Intelligence.

The development of new drugs addressing serious mental health and other disorders should avoid the psychedelic experience. Analogs of psychedelic drug...

Phenotype prediction using biologically interpretable neural networks on multi-cohort multi-omics data.

Integrating multi-omics data into predictive models has the potential to enhance accuracy, which is essential for precision medicine. In this study, w...

Automatic Detection and Assessment of Freezing of Gait Manifestations.

Freezing of gait (FOG) is an episodic and highly disabling symptom of Parkinson's disease (PD). Although described as a single phenomenon, FOG is hete...

Smoking Classification Using Novel Plasma Cytokines by implementing Machine Learning and Statistical Methods.

Smoking is a major cause of premature and preventable death. Tobacco exposure has a detrimental effect on many organs and contributes to multiple dise...

Optimization of Smoking Classification by Applying Neural Network with Variable Importance Using Cytokine Biomarkers.

Cigarette smoking is a preventable epidemic that is a leading cause of death. It increases the risk of coronary heart disease, stroke, lung cancer, ch...

Automatic detection of sleep apnea from a single-lead ECG signal based on spiking neural network model.

BACKGROUND: Sleep apnea (SLA) is a commonly encountered sleep disorder characterized by repetitive cessation of respiration while sleeping. In the pas...

Random survival forest for predicting the combined effects of multiple physiological risk factors on all-cause mortality.

Understanding the combined effects of risk factors on all-cause mortality is crucial for implementing effective risk stratification and designing targ...

Individual Predictors of Response to A Behavioral Activation-Based Digital Smoking Cessation Intervention: A Machine Learning Approach.

Depression is prevalent among individuals who smoke cigarettes and increases risk for relapse. A previous clinical trial suggests that Goal2Quit, a b...