Public Health

Machine Learning and Natural Language Processing to Improve Classification of Atrial Septal Defects in Electronic Health Records.

BACKGROUND: International Classification of Disease (ICD) codes can accurately identify patients with certain congenital heart defects (CHDs). In ICD-defined CHD data sets, the code for secundum atrial septal defect (ASD) is the most common, but it has a low positive predictive value for CHD, potentially resulting in the drawing of erroneous conclusions from such data sets. Methods with reduced fa...

Strategies in using artificial intelligence to combat antimicrobial resistance.

Infectious diseases caused by pathogens resistant to antimicrobial treatments, defined as antimicrobial resistance (AMR), are a serious global health crisis, considered among the main threats to global public health according to the World Health Organization. New forms of advanced information technology are receiving global consideration as a help in countering this health threat, like Artificial ...

A Double Machine Learning Approach for the Evaluation of COVID-19 Vaccine Effectiveness Under the Test-Negative Design: Analysis of Québec Administrative Data.

The test-negative design (TND), which is routinely used for monitoring seasonal flu vaccine effectiveness (VE), has recently become integral to COVID-...

Discovering the influence of personal features in psychological processes using Artificial Intelligence techniques: the case of COVID19 lockdown in Spain

At the end of 2019, an outbreak of a novel coronavirus was reported in China, leading to the COVID-19 pandemic. In Spain, the first cases were detec...

Cancer Vaccine Adjuvant Name Recognition from Biomedical Literature using Large Language Models

Motivation: An adjuvant is a chemical incorporated into vaccines that enhances their efficacy by improving the immune response. Identifying adjuvant...

USRNet: Unified Scene Recovery Network for Enhancing Traffic Imaging under Multiple Adverse Weather Conditions

Advancements in computer vision technology have facilitated the extensive deployment of intelligent transportation systems and visual surveillance s...

A Fair and Optimal Approach to Sequential Healthcare Rationing

The COVID-19 pandemic underscored the urgent need for fair and effective allocation of scarce resources, from hospital beds to vaccine distribution....

Social inequality and cultural factors impact the awareness and reaction during the cryptic transmission period of pandemic

The World Health Organization (WHO) declared the COVID-19 outbreak a Public Health Emergency of International Concern (PHEIC) on January 31, 2020. H...

Synthetic Datasets for Machine Learning on Spatio-Temporal Graphs using PDEs

Many physical processes can be expressed through partial differential equations (PDEs). Real-world measurements of such processes are often collecte...

Mining Unstructured Medical Texts With Conformal Active Learning

The extraction of relevant data from Electronic Health Records (EHRs) is crucial to identifying symptoms and automating epidemiological surveillance...

A Novel Zero-Touch, Zero-Trust, AI/ML Enablement Framework for IoT Network Security

The IoT facilitates a connected, intelligent, and sustainable society; therefore, it is imperative to protect the IoT ecosystem. The IoT-based 5G an...

Invited commentary: deep learning-methods to amplify epidemiologic data collection and analyses.

Deep learning is a subfield of artificial intelligence and machine learning, based mostly on neural networks and often combined with attention algorit...

Hepatitis C Virus Saint Petersburg Variant Detection With Machine Learning Methods.

Hepatitis C virus infection is a significant global health concern, affecting millions worldwide. Although direct-acting antivirals achieve over 90% s...

Global Epidemiology of Outbreaks of Unknown Cause Identified by Open-Source Intelligence, 2020-2022.

Epidemic surveillance using traditional approaches is dependent on case ascertainment and is delayed. Open-source intelligence (OSINT)-based syndromic...

Graph Learning for Bidirectional Disease Contact Tracing on Real Human Mobility Data

For rapidly spreading diseases where many cases show no symptoms, swift and effective contact tracing is essential. While exposure notification appl...

An Explainable Disease Surveillance System for Early Prediction of Multiple Chronic Diseases

This study addresses a critical gap in the healthcare system by developing a clinically meaningful, practical, and explainable disease surveillance ...

Graph-Based Cross-Domain Knowledge Distillation for Cross-Dataset Text-to-Image Person Retrieval

Video surveillance systems are crucial components for ensuring public safety and management in smart city. As a fundamental task in video surveillan...

Reducing Size Bias in Sampling for Infectious Disease Spread on Networks

Epidemiological models can aid policymakers in reducing disease spread by predicting outcomes based on disease dynamics and contact network characte...

Modality Unified Attack for Omni-Modality Person Re-Identification

Deep learning based person re-identification (re-id) models have been widely employed in surveillance systems. Recent studies have demonstrated that...

DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention

While tobacco advertising innovates at unprecedented speed, traditional surveillance methods remain frozen in time, especially in the context of soc...