Public Health & Policy

Decoding Data Science Upskilling: Insights From 5 Years of Data Science Projects at the Centers for Disease Control and Prevention, 2019-2023.

CONTEXT: Public health organizations are increasingly recognizing the value and potential of data science. However, a gap remains in understanding how data science is being applied in public health. OBJECTIVE: This article provides a comprehensive overview of data science applications in real-world public health settings. By describing the characteristics of projects supported by the Centers for D...

Regulatory Challenges and Opportunities: A Review of U.S. Food and Drug Administration-Approved Artificial Intelligence and Machine Learning-Enabled Cardiovascular Devices.

The integration of artificial intelligence (AI) and machine learning (ML) into medical devices has revolutionized healthcare, enhancing diagnostic accuracy and clinical decision-making. However, their rapid evolution poses challenges to traditional regulatory frameworks, particularly in ensuring safety and effectiveness. This review examines current Food and Drug Administration (FDA) regulatory pa...

Clinically oriented deep learning framework for automated vessel wall segmentation in black-blood MRI: a multi-center study.

OBJECTIVES: To develop and validate a clinically applicable deep learning framework for automated segmentation of intracranial and carotid vessel wall...

Global trends in artificial intelligence applications for gastric cancer prediction, treatment, and management: a topic modeling and bibliometric analysis.

PURPOSE: Artificial intelligence (AI) emerged as a promising tool for enhancing healthcare delivery and outcomes for gastric cancer (GC) patients. Thi...

Bridging the Hearing Divide: Policy Solutions for Aging Americans.

Hearing loss affects approximately two thirds of adults in the United States aged 70 years or older and frequently remains untreated despite its well-...

Integrating multiphase MRI surrogates to improve microvascular invasion detection and recurrence risk stratification in HCC.

BACKGROUND & AIMS: Microvascular invasion (MVI) is a key determinant of recurrence and poor outcomes in hepatocellular carcinoma (HCC), yet accurate p...

From passive prevention to proactive intervention: An AI-assisted integrated governance system for algal bloom monitoring and control.

Algal blooms, characterized by the excessive proliferation of microalgae in a freshwater or marine ecosystem, have evolved into a global ecological, h...

Feasibility of deep learning-accelerated HASTE-FS for pancreatic cystic lesion surveillance: comparison with conventional HASTE and MRCP.

PURPOSE: Pancreatic cystic lesions (PCL) commonly undergo surveillance using MRI with MR cholangiopancreatography (MRCP). Our objective is to compare ...

Utility and limitations of the FDA adverse events reporting system public dashboard for safety analyses: a case study with vesicular monoamine transporter 2 inhibitors.

INTRODUCTION: The United States Food and Drug Administration (FDA) requires post-marketing surveillance of approved drugs, and pharmaceutical manufact...

Suicidal thoughts and behaviours among construction workers: Identifying risks and protective factors.

BACKGROUND: The construction industry records concerningly high rates of suicide compared to other industries. This study aimed to (1) identify sub-gr...

Predictive surveillance and diagnosis of COVID-19: An integrative machine learning and wastewater multi-omics approach.

COVID-19 has had major global impacts, highlighting the importance of robust predictive surveillance and diagnostic systems to ensure effective public...

From wastewater to epidemiological insights: A systematic review of modeling strategies for infectious disease surveillance.

Wastewater-based epidemiology (WBE) has emerged as a promising complementary tool in infectious diseases surveillance systems, offering real-time insi...

Deep learning for automatic detection of hepatocellular carcinoma in dynamic contrast-enhanced MRI.

OBJECTIVE: Develop a deep learning model for automatic hepatocellular carcinoma (HCC) detection in T1 weighted imaging (WI) Dynamic Contrast-Enhanced ...

Machine Learning-Based Risk Prediction for Feline Mammary Tumours: A Comprehensive Epidemiological Analysis Using Multi-Model Ensemble Approach.

Feline mammary tumours represent the third most common malignancy in cats, with limited evidence-based tools available for risk assessment and screeni...

Optimal adherence thresholds for oral anticoagulants in patients with atrial fibrillation using machine learning and population administrative data.

BACKGROUND: Adherence to oral anticoagulants (OACs) for atrial fibrillation (AF) stroke prevention is traditionally defined as taking 80% of doses as ...

Development of explainable machine learning-based prediction models of depression with mild and severe insomnia in middle-aged and elderly Chinese people.

BACKGROUND: The risk of depression is significantly elevated in middle-aged and older adults with insomnia; however, the pathways between mild and sev...

Predicting Heart Failure From 12-Lead ECGs Using AI: A HeartShare/AMP-HF Pooled Cohort Analysis.

BACKGROUND: Artificial intelligence applied to electrocardiograms (ECG-AI) offers a scalable approach to identify individuals at risk for heart failur...

Zoonoticus: A machine learning model for genomic prediction of zoonotic bacterial strains using virulence, resistance, and mobile genetic elements.

Zoonotic diseases continue to rise globally, yet no existing genomic tool integrates virulence, antimicrobial resistance (AMR), and mobile genetic ele...

Flavonoid-modulated JAK-STAT signaling mitigates malignant transformation and drug resistance in breast tumors: A clinically relevant 3PM-guided innovation.

BACKGROUND: Breast cancer (BC) treatment efficacy is often compromised by tumor cell plasticity and multidrug resistance of multi-factorial origin. Am...

The Development and Validation of Multivariable Electronic Health Record-Based Models to Predict Diabetic Ketoacidosis-Related Hospitalizations for Adults with Type 1 Diabetes.

AIM: To develop and validate models that use electronic health record (EHR) data to predict diabetic ketoacidosis (DKA)-related hospitalizations over ...