Latest AI and machine learning research in smoking & tobacco for healthcare professionals.
The nicotine addiction public health crisis continues to be pervasive. In this century alone, the tobacco industry has released and marketed new products in an aggressive effort to lure new and young customers for life. Such innovations and product development, namely flavored nicotine or tobacco such as nicotine pouches, have undone years of anti-tobacco campaign work. Past work is limited both i...
Objective: Quantitative computed tomography (QCT) can automatically quantify parenchymal abnormalities on chest CT imaging using deep learning. We leveraged QCT to detect pulmonary abnormalities in patients with early rheumatoid arthritis (RA) compared to healthy controls. Methods: We analyzed high-resolution CT chest imaging from participants with early RA in the prospective, multicenter, SAIL-RA...
Background: Advances in wearable devices and machine-learning-based ECG analysis enable highly accurate detection of atrial fibrillation (AF) outside ...
Depression is a heterogeneous disorder, often diagnosed based on symptom co-occurrence. However, individuals may present with markedly different sympt...
Background: Liver cancer primarily develops in patients with chronic liver disease (CLD), yet most cases are diagnosed at an advanced stage with poor ...
Background: Cardiovascular diseases (CVDs) remain the leading global cause of morbidity and mortality. In clinical practice, 10-year risk prediction t...
The ventral tegmental area is the primary source of dopaminergic input to the human prefrontal cortex and plays a central role in reinforcement learni...
Background: Chronic inflammation predicts adverse cardiovascular outcomes, but mechanisms linking systemic inflammation to cardiac remodeling remain i...
Purpose: Studies based on electronic health records (EHR) often rely on structured data, which may incompletely capture important clinical phenotypes ...
IntroductionAcute myocardial infarction (AMI) remains a leading cause of mortality, with the coexistence of other conditions (i.e., multimorbidity) co...
BACKGROUND: Tobacco use remains a significant global health challenge, contributing substantially to preventable morbidity and mortality. Despite esta...
Machine learning methods are increasingly applied to analyze health-related public discourse based on large-scale data, but questions remain regardi...
The use of reinforcement learning (RL) methods to support health behavior change via personalized and just-in-time adaptive interventions is of sign...
Social determinants of health (SDoH) significantly influence health outcomes, shaping disease progression, treatment adherence, and health dispariti...
Pancreatic ductal adenocarcinoma (PDAC) is recognized as one of the most lethal malignancies, characterized by late-stage diagnosis and limited therap...
Comorbidity networks, which capture disease-disease co-occurrence usually based on electronic health records, reveal structured patterns in how dise...
$\textbf{Objective:}$ Brain-predicted age difference (BrainAGE) is a neuroimaging biomarker reflecting brain health. However, training robust BrainA...
Lung cancer is one of the leading causes of cancer-related mortality worldwide, with most cases diagnosed at advanced stages where curative treatment ...
Social determinants of health (SDOH) extraction from clinical text is critical for downstream healthcare analytics. Although large language models (...
BACKGROUND: Lung cancer (LC) is the second most diagnosed cancer and the leading cause of cancer mortality worldwide. Non-small cell lung cancer (NSCL...