Public Health & Policy

Enforcing Quality in Strain Imaging Through AI-Powered Surveillance.

COVID-19 Surveillance in a Primary Care Sentinel Network: In-Pandemic Development of an Application Ontology.

BACKGROUND: Creating an ontology for COVID-19 surveillance should help ensure transparency and consi...

Methodological considerations in MVC epidemiological research.

BACKGROUND: The global burden of MVC injuries and deaths among vulnerable road users, has led to the...

Artificial Intelligence-Electrocardiography to Predict Incident Atrial Fibrillation: A Population-Based Study.

BACKGROUND: An artificial intelligence (AI) algorithm applied to electrocardiography during sinus rh...

Repurposing therapeutics for COVID-19: Rapid prediction of commercially available drugs through machine learning and docking.

BACKGROUND: The outbreak of the novel coronavirus disease COVID-19, caused by the SARS-CoV-2 virus h...

Automatic detection of COVID-19 from chest radiographs using deep learning.

INTRODUCTION: The breakdown of a deadly infectious disease caused by a newly discovered coronavirus ...

Public Perception of the COVID-19 Pandemic on Twitter: Sentiment Analysis and Topic Modeling Study.

BACKGROUND: COVID-19 is a scientifically and medically novel disease that is not fully understood be...

Closing the Digital Health Evidence Gap: Development of a Predictive Score to Maximize Patient Outcomes.

Clinical studies of telemedicine (TM) programs for chronic illness have demonstrated mixed results ...

Early gastric cancer and Artificial Intelligence: Is it time for population screening?

Gastric cancer is a common cause of death worldwide and its early detection is crucial to improve it...

Comparing machine learning with case-control models to identify confirmed dengue cases.

In recent decades, the global incidence of dengue has increased. Affected countries have responded w...

Smartphone Motion Sensor-Based Complex Human Activity Identification Using Deep Stacked Autoencoder Algorithm for Enhanced Smart Healthcare System.

Human motion analysis using a smartphone-embedded accelerometer sensor provided important context fo...

The opportunities and challenges of machine learning in the acute care setting for precision prevention of posttraumatic stress sequelae.

Personalized medicine is among the most exciting innovations in recent clinical research, offering t...

Tree-Based Machine Learning to Identify and Understand Major Determinants for Stroke at the Neighborhood Level.

Background Stroke is a major cardiovascular disease that causes significant health and economic burd...

Influenza Screening via Deep Learning Using a Combination of Epidemiological and Patient-Generated Health Data: Development and Validation Study.

BACKGROUND: Screening for influenza in primary care is challenging due to the low sensitivity of rap...

Measuring and Preventing COVID-19 Using the SIR Model and Machine Learning in Smart Health Care.

COVID-19 presents an urgent global challenge because of its contagious nature, frequently changing c...

Yi Zeng: promoting good governance of artificial intelligence.

Artificial intelligence (AI) has developed quickly in recent years, with applications expanding from...

Forecasting COVID-19 outbreak progression using hybrid polynomial-Bayesian ridge regression model.

In 2020, Coronavirus Disease 2019 (COVID-19), caused by the SARS-CoV-2 (Severe Acute Respiratory Syn...

Using Machine Learning to Predict Suicide Attempts in Military Personnel.

Identifying predictors of suicide attempts is critical in intervention and prevention efforts, yet f...

Artificial Intelligence, Big Data, and mHealth: The Frontiers of the Prevention of Violence Against Children.

Violence against children is a global public health threat of considerable concern. At least half of...

Telerobotic ultrasound to provide obstetrical ultrasound services remotely during the COVID-19 pandemic.

INTRODUCTION: Obstetrical ultrasound imaging is critical in identifying at-risk pregnancies and info...

Extracting medication information from unstructured public health data: a demonstration on data from population-based and tertiary-based samples.

BACKGROUND: Unstructured data from clinical epidemiological studies can be valuable and easy to obta...