Smoking & Tobacco

Predictive modeling for peri-implantitis by using machine learning techniques.

The purpose of this retrospective cohort study was to create a model for predicting the onset of peri-implantitis by using machine learning methods and to clarify interactions between risk indicators. This study evaluated 254 implants, 127 with and 127 without peri-implantitis, from among 1408 implants with at least 4 years in function. Demographic data and parameters known to be risk factors for ...

Breast cancer risk prediction in African women using Random Forest Classifier.

INTRODUCTION: One of the most important steps in combating breast cancer is early and accurate diagnosis. Unfortunately, breast cancer is asymptomatic at the early stage, although some symptoms are presented at a later time, but at symptomatic stage treatment could be complicated or even become impossible thereby leading to death. Proper risk assessment is hence very important in reducing mortalit...

Digital Data Sources and Their Impact on People's Health: A Systematic Review of Systematic Reviews.

Digital data sources have become ubiquitous in modern culture in the era of digital technology but often tend to be under-researched because of restr...

Machine learning and bioinformatic analysis of brain and blood mRNA profiles in major depressive disorder: A case-control study.

This study analyzed gene expression messenger RNA data, from cases with major depressive disorder (MDD) and controls, using supervised machine learnin...

Explainable AI reveals changes in skin microbiome composition linked to phenotypic differences.

Alterations in the human microbiome have been observed in a variety of conditions such as asthma, gingivitis, dermatitis and cancer, and much remains ...

Assessment of Thoracic Pain Using Machine Learning: A Case Study from Baja California, Mexico.

Thoracic pain is a shared symptom among gastrointestinal diseases, muscle pain, emotional disorders, and the most deadly: Cardiovascular diseases. Due...

Improving the Utility of Tobacco-Related Problem List Entries Using Natural Language Processing.

We present findings on using natural language processing to classify tobacco-related entries from problem lists found within patient's electronic heal...

Artificial intelligence matches subjective severity assessment of pneumonia for prediction of patient outcome and need for mechanical ventilation: a cohort study.

To compare the performance of artificial intelligence (AI) and Radiographic Assessment of Lung Edema (RALE) scores from frontal chest radiographs (CXR...

Assessing PD-L1 expression in non-small cell lung cancer and predicting responses to immune checkpoint inhibitors using deep learning on computed tomography images.

This study aimed to use computed tomography (CT) images to assess PD-L1 expression in non-small cell lung cancer (NSCLC) and predict response to immu...

A machine learning compatible method for ordinal propensity score stratification and matching.

Although machine learning techniques that estimate propensity scores for observational studies with multivalued treatments have advanced rapidly in re...

Machine Learning Revealed New Correlates of Chronic Pelvic Pain in Women.

Chronic pelvic pain affects one in seven women worldwide, and there is an urgent need to reduce its associated significant costs and to improve women'...

Evaluation of incomplete maternal smoking data using machine learning algorithms: a study from the Medical Birth Registry of Norway.

BACKGROUND: The Medical Birth Registry of Norway (MBRN) provides national coverage of all births. While retrieval of most of the information in the bi...

Prediction of vascular aging based on smartphone acquired PPG signals.

Photoplethysmography (PPG) measured by smartphone has the potential for a large scale, non-invasive, and easy-to-use screening tool. Vascular aging is...

Radiomic Detection of EGFR Mutations in NSCLC.

Radiomics is defined as the use of automated or semi-automated post-processing and analysis of multiple features derived from imaging exams. Extracted...

Predicting the first smoking lapse during a quit attempt: A machine learning approach.

BACKGROUND: Just-in-time adaptive interventions (JITAI) aim to prevent smoking lapse using tailored support delivered via mobile technology in the mom...

On the performance of fusion based planet-scope and Sentinel-2 data for crop classification using inception inspired deep convolutional neural network.

This research work aims to develop a deep learning-based crop classification framework for remotely sensed time series data. Tobacco is a major revenu...

Machine Learning-Based DNA Methylation Score for Fetal Exposure to Maternal Smoking: Development and Validation in Samples Collected from Adolescents and Adults.

BACKGROUND: Fetal exposure to maternal smoking during pregnancy is associated with the development of noncommunicable diseases in the offspring. Mater...

Correlation between the extent of smoking, salivary protein profiles, and dental caries in young adult smokers.

UNLABELLED: Context.Proteins in the saliva are one of the defense mechanism factors that can protect the oral cavity from disease. However, smoking mi...

XGBoost-Based Framework for Smoking-Induced Noncommunicable Disease Prediction.

Smoking-induced noncommunicable diseases (SiNCDs) have become a significant threat to public health and cause of death globally. In the last decade, n...

Deep Learning Using Chest Radiographs to Identify High-Risk Smokers for Lung Cancer Screening Computed Tomography: Development and Validation of a Prediction Model.

BACKGROUND: Lung cancer screening with chest computed tomography (CT) reduces lung cancer death. Centers for Medicare & Medicaid Services (CMS) eligib...