Public Health & Policy

Nationwide Prediction of Missed and Cancelled Appointments Using Real-World EHR Data

ObjectivesTo develop and evaluate predictive models for unused outpatient appointments (missed or cancelled) using a large national electronic health record (EHR) repository in the United States. DesignRetrospective observational study using machine learning and statistical modeling. SettingA U.S. national electronic health record repository (Cerner Real World Database) covering healthcare encou...

Development and evaluation of CADe systems in low-prevalence setting: The RARE25 challenge for early detection of Barrett's neoplasia

Computer-aided detection (CADe) of early neoplasia in Barrett's esophagus is a low-prevalence surveillance problem in which clinically relevant findings are rare. Although many CADe systems report strong performance on balanced or enriched datasets, their behavior under realistic prevalence remains insufficiently characterized. The RARE25 challenge addresses this gap by introducing a large-scale, ...

Automatic Generation of Executable BPMN Models from Medical Guidelines

We present an end-to-end pipeline that converts healthcare policy documents into executable, data-aware Business Process Model and Notation (BPMN) mod...

Physical Adversarial Attacks on AI Surveillance Systems:Detection, Tracking, and Visible--Infrared Evasion

Physical adversarial attacks are increasingly studied in settings that resemble deployed surveillance systems rather than isolated image benchmarks. I...

SurFITR: A Dataset for Surveillance Image Forgery Detection and Localisation

We present the Surveillance Forgery Image Test Range (SurFITR), a dataset for surveillance-style image forgery detection and localisation, in response...

Toward Unified Fine-Grained Vehicle Classification and Automatic License Plate Recognition

Extracting vehicle information from surveillance images is essential for intelligent transportation systems, enabling applications such as traffic mon...

TELF: An End-to-End Temporal Encoder with Late Fusion for Interpretable Disease Risk Prediction from Longitudinal Real-World Data

Deep learning models utilizing longitudinal healthcare data have significantly advanced epidemiological research. However, contemporary transformer-ba...

Cardiovascular Adverse Events After Definitive Chemoradiotherapy for Lung Cancer in an Appalachian Population: Incidence and Machine Learning Based Prediction

Background Cardiovascular adverse events (CVAEs) after chemoradiotherapy (CRT) for lung cancer are major concerns in Appalachia due to high rates of s...

Predicting COVID-19 incidence from seroprevalence and population-based cohort data using interpretable machine learning with differential privacy analysis

During the COVID-19 pandemic, reported incidence data played a central role in public health surveillance and in tracking epidemic dynamics, although ...

Governance, Accountability and Post-Deployment Monitoring Preferences for AI Integration in West African Clinical Practice: A Mixed-Methods Study

Background: The integration of artificial intelligence (AI) into clinical practice holds transformative potential for healthcare in West Africa, but s...

Development and Pilot Validation of ABHA-O-SHINE: An AI-Ready Oral Health Risk and Insurance Prediction Framework within the Ayushman Bharat Digital Ecosystem

Background: Oral health remains inadequately integrated within the Ayushman Bharat Digital Mission (ABDM), particularly in terms of structured risk as...

Predicting highly pathogenic avian influenza H5N1 outbreak risk using extreme weather and bird migration data in machine learning models

Background. Climate change is intensifying extreme weather events (EWEs) with potentially profound consequences for zoonotic disease dynamics, yet the...

BSO-AD: An Ontology for Representing and Harmonizing Behavioral Social Knowledge in ADRD

Objective: Behavioral and social factors (BSFs) substantially influence the risk, onset, and progression of Alzheimer disease and related dementias (A...

MedResearchBench: A Multi-Domain Benchmark for Evaluating AI Research Agents on Clinical Medical Research

The rapid advancement of AI research automation systems--including AI Scientist, data-to-paper, and Agent Laboratory--has demonstrated the potential f...

Deciphering Environmental Health Factors Behind Unknown Etiology of Chronic Kidney Disease in South Asia: Plans for Epidemiologic Study

Chronic kidney disease of unknown etiology (CKDu) has emerged as an important public health challenge, particularly in agricultural communities across...

faers: A High-Fidelity Framework and R/Bioconductor Package for Precision Adverse Event Surveillance

Background: The FDA Adverse Event Reporting System (FAERS) is a critical pillar of post-marketing pharmacovigilance; however, its utility is constrain...

From Pixels to Privacy: Temporally Consistent Video Anonymization via Token Pruning for Privacy Preserving Action Recognition

Recent advances in large-scale video models have significantly improved video understanding across domains such as surveillance, healthcare, and enter...

Classification of Recurrence Status After Surgical Treatment of Chronic Subdural Hemorrhage - A Machine Learning Approach

Background Chronic subdural hematoma (cSDH) recurrence requiring reoperation occurs in 5-33% of cases, representing a substantial clinical and economi...

Development of a natural language processing application to extract and categorize mentions of violence from mental healthcare records text

Background: Experiences of violence are reported frequently by mental health service users, victims of violence are at a greater risk of mental health...

SDD-YOLO: A Small-Target Detection Framework for Ground-to-Air Anti-UAV Surveillance with Edge-Efficient Deployment

Detecting small unmanned aerial vehicles (UAVs) from a ground-to-air (G2A) perspective presents significant challenges, including extremely low pixel ...