Public Health & Policy

Machine learning-based prediction model for patients with recurrent Staphylococcus aureus bacteremia.

BACKGROUND: Staphylococcus aureus bacteremia (SAB) remains a significant contributor to both community-acquired and healthcare-associated bloodstream infections. SAB exhibits a high recurrence rate and mortality rate, leading to numerous clinical treatment challenges. Particularly, since the outbreak of COVID-19, there has been a gradual increase in SAB patients, with a growing proportion of (Meth...

Utilizing natural language processing for precision prevention of mental health disorders among youth: A systematic review.

BACKGROUND: The global mental health crisis has created barriers to youth mental healthcare, leaving many disorders unaddressed. Precision prevention, which identifies individual risks, offers the potential for tailored interventions. While natural language processing (NLP) has shown promise in the early detection of mental health disorders, no review has examined its role in youth mental health d...

Predicting sleep quality among college students during COVID-19 lockdown using a LASSO-based neural network model.

BACKGROUND: In March 2022, a new outbreak of COVID-19 emerged in Quanzhou, leading to the implementation of strict lockdown management measures in col...

Advanced applications in chronic disease monitoring using IoT mobile sensing device data, machine learning algorithms and frame theory: a systematic review.

The escalating demand for chronic disease management has presented substantial challenges to traditional methods. However, the emergence of Internet o...

The Mechanism of Bisphenol S-Induced Atherosclerosis Elucidated Based on Network Toxicology, Molecular Docking, and Machine Learning.

The increasing prevalence of environmental pollutants has raised public concern about their potential role in diseases such as atherosclerosis (AS). E...

Biomarkers, omics and artificial intelligence for early detection of pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is frequently diagnosed in its late stages when treatment options are limited. Unlike other common cancers, th...

Proteome analysis of the prefrontal cortex and the application of machine learning models for the identification of potential biomarkers related to suicide.

INTRODUCTION: Suicide is a significant public health problem, with increased rates in low- and middle-income countries such as Mexico; therefore, suic...

Diagnosis of Acute Appendicitis with Machine Learning-Based Computer Tomography: Diagnostic Reliability and Role in Clinical Management.

Acute appendicitis (AA) is a common surgical emergency affecting 7-8% of the population. Timely diagnosis and treatment are crucial for preventing se...

The Role of Artificial Intelligence in Obesity Risk Prediction and Management: Approaches, Insights, and Recommendations.

Greater than 650 million individuals worldwide are categorized as obese, which is associated with significant health, economic, and social challenges....

Artificial intelligence for modelling infectious disease epidemics.

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related te...

Predicting mental health disparities using machine learning for African Americans in Southeastern Virginia.

This study examined mental health disparities among African Americans using AI and machine learning for outcome prediction. Analyzing data from Africa...

Redefining prostate cancer care: innovations and future directions in active surveillance.

PURPOSE OF REVIEW: This review provides a critical analysis of recent advancements in active surveillance (AS), emphasizing updates from major interna...

Projections of single-level indirect lumbar interbody fusion volume and associated costs for Medicare patients to 2050.

BACKGROUND: Anterior, Oblique, and Lateral Lumbar Interbody Fusions (ALIF/OLIF/LLIF) are powerful modalities for achieving indirect decompression of l...

Machine Learning Approach to Identifying Wrong-Site Surgeries Using Centers for Medicare and Medicaid Services Dataset: Development and Validation Study.

BACKGROUND: Wrong-site surgery (WSS) is a critical but preventable medical error, often resulting in severe patient harm and substantial financial cos...

Machine-learning approaches to predict individualized treatment effect using a randomized controlled trial.

Recent advancements in machine learning (ML) for analyzing heterogeneous treatment effects (HTE) are gaining prominence within the medical and epidemi...

Early detection of disease outbreaks and non-outbreaks using incidence data: A framework using feature-based time series classification and machine learning.

Forecasting the occurrence and absence of novel disease outbreaks is essential for disease management, yet existing methods are often context-specific...

Of Lyme disease and machine learning in a One Health world.

OBJECTIVE: Lyme disease is a vector-borne emerging zoonosis in Ontario driven by human population growth and climate change. Lyme disease is also a pr...

MedFuseNet: fusing local and global deep feature representations with hybrid attention mechanisms for medical image segmentation.

Medical image segmentation plays a crucial role in addressing emerging healthcare challenges. Although several impressive deep learning architectures ...

Low-Power and Low-Cost AI Processor With Distributed-Aggregated Classification Architecture for Wearable Epilepsy Seizure Detection.

Wearable devices with continuous monitoring capabilities are critical for the daily detection of epileptic seizures, as they provide users with accura...

Machine learning-enhanced surface-enhanced spectroscopic detection of polycyclic aromatic hydrocarbons in the human placenta.

The detection and identification of polycyclic aromatic hydrocarbons (PAHs) and their derivatives, polycyclic aromatic compounds (PACs), are essential...