Latest AI and machine learning research in public health & policy for healthcare professionals.
Precision-guided dual antiplatelet therapy (DAPT) duration post-percutaneous coronary intervention (PCI) remains a clinical challenge. Current risk stratification methods lack personalization, underscoring the need for advanced predictive tools. We developed and validated an artificial intelligence (AI) framework (LightGBM, random forest [RF], logistic regression [LR]) to optimize DAPT duration us...
Coronary heart disease (CHD) remains the leading cause of mortality worldwide, disproportionately affecting low- and middle-income countries where diagnostic resources are limited. Traditional statistical models often fail to deliver adequate predictive accuracy in complex, high-dimensional, and imbalanced health datasets. To develop and evaluate enhanced machine learning and hybrid ensemble model...
Sepsis remains a leading cause of intensive care unit (ICU) mortality worldwide, and early detection is essential for improving survival through timel...
Pneumonia is a respiratory condition characterized by inflammation of the alveolar sacs in the lungs, which disrupts normal oxygen exchange. This dise...
The interaction between physical activity and sleep with cardiovascular disease remains poorly understood, despite both being key risk factors. This s...
Pancreatic cystic lesions (PCLs) are often discovered incidentally on imaging and may progress to pancreatic ductal adenocarcinoma (PDAC). PCLs have a...
This study aimed to predict body mass index (BMI) trajectories from childhood to early adulthood using explainable artificial intelligence, integratin...
An increase in syphilis cases in the United States and the global shortage of Benzathine Penicillin G (BPG) calls for evidence-based optimization. Con...
The lack of understanding of how individuals communicate suicidal stress hinders global suicide intervention plans and practices. This study identifie...
Empirical methods of chaos theory have been applied to epidemiological data, uncovering evidence of chaos. In the current work, we apply, to the weekl...
Adequate self-harm surveillance is a key part of suicide prevention efforts. Our prior work has demonstrated the efficacy of an artificial intelligenc...
The early detection of adverse drug events (ADEs) became a critical issue in clinical research after the thalidomide disaster in 1961, which resulted ...
Influenza burden in subtropical regions like southeastern China is shaped by meteorological factors-driven complex transmission patterns that differ f...
Musculoskeletal conditions are a leading global cause of disability, yet the factors influencing long-term musculoskeletal health, particularly follow...
The timely detection of ward deterioration—including unplanned intensive care unit (ICU) transfer, cardiac arrest, death, and sepsis—remains an unmet ...
We aimed to conduct a comprehensive genomic analysis of ceftolozane/tazobactam (C/T) resistance mechanisms in Pseudomonas aeruginosa by combining nove...
Kawasaki disease (KD) is an acute, pediatric vasculitis associated with coronary artery abnormality (CAA) development. Echocardiography at month 1 pos...
Postpartum hemorrhage (PPH) is a major cause of maternal morbidity and mortality. Timely prediction may prevent adverse maternal outcomes, and efforts...
Precision medicine promises significant health benefits but faces challenges such as complex data management and analytics, interdisciplinary collabor...
Perioperative cardiac arrest (CA) is a devastating surgical complication, yet its epidemiology and risk factors across diverse surgical populations ar...