Smoking & Tobacco

A hybrid computer vision model to predict lung cancer in diverse populations

Disparities of lung cancer incidence exist in Black populations and screening criteria underserve Black populations due to disparately elevated risk in the screening eligible population. Prediction models that integrate clinical and imaging-based features to individualize lung cancer risk is a potential means to mitigate these disparities. This Multicenter (NLST) and catchment population based (UI...

Evaluating Feature Selection Methods and Feature Contributions for Cardiovascular Disease Risk Prediction

Cardiovascular disease (CVD) remains the foremost contributor to global illness and death, underscoring the critical need for effective tools that can predict risk at early stages to support preventive care and timely clinical decisions. With the growing complexity of healthcare data, machine learning has shown considerable promise in extracting insights that enhance medical decision-making. Nonet...

Closing the Lung Cancer Screening Gap in FQHCs with AI-Powered Clinical Decision Support

Lung cancer remains the leading cause of cancer-related mortality in the United States, with screening adherence rates below 16% nationally and even l...

Automated Deep Learning-Based Detection of Early Atherosclerotic Plaques in Carotid Ultrasound Imaging

Carotid plaque presence is associated with cardiovascular risk, even among asymptomatic individuals. While deep learning has shown promise for carotid...

Socioeconomic and Behavioral Drivers of Geographic Disparities in U.S. Cardiovascular Mortality: A Machine Learning Analysis

Substantial geographic disparities in cardiovascular disease (CVD) mortality persist across the United States. The extent to which “place” reflects un...

Dementia Risk and Machine Learning-Derived Brain Age Index from Sleep Electroencephalography: A Pooled Cohort Analysis of Over 7,000 Individuals Across Five Community Cohorts

Sleep electroencephalographic (EEG) microstructures are closely related to cognition and undergo age-dependent changes. However, their multidimensiona...

Development of a Hypertension Risk Prediction Model using Nationally Representative Survey Data: A Machine Learning Approach and Web Application Deployment

Hypertension is a major modifiable risk factor for cardiovascular diseases. Early identification of high-risk individuals using predictive models can ...

Privacy-Enhancing Sequential Learning under Heterogeneous Selection Bias in Multi-Site EHR Data

To develop privacy-enhancing statistical methods for estimation of binary disease risk model association parameters across multiple electronic health ...

Predicting Vaping Cessation in Young Adults: A Machine Learning and Explainable Artificial Intelligence (XAI) Approach to Public Health Intervention

The public health impact of vaping in the United States reflects a complex balance of potential benefits and emerging risks. While e-cigarettes can su...

Childhood Maltreatment and Risk for Illicit Substance Use: Evidence for Mid-Adolescence as a Sensitive Exposure Period

Childhood maltreatment is a well-established risk factor for substance misuse. However, it remains unclear whether risk for specific illicit substance...

Science or Advocacy? The Global Rise of Policy Claims in Population Health Research (1990-2024)

Should original research routinely contain prominent policy claims, such as recommendations for policymakers or broad calls to action? Growing emphasi...

Shared genetic architecture of brain age gap across 30 cohorts worldwide

Deviations from normative brain ageing trajectories are linked to a wide range of adverse health outcomes. A number of brain age prediction models hav...

ExCaPT: Explainable Cancer Prediction with Transformer-based models

Cancer remains one of the most significant global health challenges. De-spite advances in treatment, early detection remains a critical concern. The i...

Evaluation of SSI risk prediction model after spinal surgery: A systematic review and critical appraisal

This study aimed to systematically review and critically evaluate the risk of bias and applicability of surgical site infection (SSI) risk prediction ...

Cytopathological quantification of NORs using artificial intelligence to oral cancer screening.

Oral squamous cell carcinoma (OSCC) remains the most prevalent neoplasm of the head and neck. In recent decades, the incidence and prevalence of OSCC ...

WordVIS: A Color Worth A Thousand Words

Document classification is considered a critical element in automated document processing systems. In recent years multi-modal approaches have becom...

Allium Vegetables Intake and Digestive System Cancer Risk: A Study Based on Mendelian Randomization, Network Pharmacology and Molecular Docking

Background: Allium vegetables (garlic and onion) are one of the flavorings in people's daily diets. Observational studies suggest that intake of all...

Graphical Structural Learning of rs-fMRI data in Heavy Smokers

Recent studies revealed structural and functional brain changes in heavy smokers. However, the specific changes in topological brain connections are...

Multimodal ischemic stroke recurrence prediction model based on the capsule neural network and support vector machine.

Ischemic stroke (IS) has a high recurrence rate. Machine learning (ML) models have been developed based on single-modal biochemical tests, and imaging...

Identification of Prognostic Biomarkers for Stage III Non-Small Cell Lung Carcinoma in Female Nonsmokers Using Machine Learning

Lung cancer remains a leading cause of cancer-related deaths globally, with non-small cell lung cancer (NSCLC) being the most common subtype. This s...