Public Health & Policy

A Methodology Based on Expert Systems for the Early Detection and Prevention of Hypoxemic Clinical Cases.

Respiratory diseases are currently considered to be amongst the most frequent causes of death and disability worldwide, and even more so during the year 2020 because of the COVID-19 global pandemic. Aiming to reduce the impact of these diseases, in this work a methodology is developed that allows the early detection and prevention of potential hypoxemic clinical cases in patients vulnerable to res...

EventEpi-A natural language processing framework for event-based surveillance.

According to the World Health Organization (WHO), around 60% of all outbreaks are detected using informal sources. In many public health institutes, including the WHO and the Robert Koch Institute (RKI), dedicated groups of public health agents sift through numerous articles and newsletters to detect relevant events. This media screening is one important part of event-based surveillance (EBS). Rea...

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 consistency. Ontologies formalize conceptualizations at...

Methodological considerations in MVC epidemiological research.

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

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

BACKGROUND: An artificial intelligence (AI) algorithm applied to electrocardiography during sinus rhythm has recently been shown to detect concurrent ...

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 has spread rapidly around the globe during the past...

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 (named SARS n-CoV2) back in December 2019 has show...

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 because it has yet to be consistently and deeply stu...

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 across settings and populations. With recent uptak...

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 its prognosis. Its incidence varies throughout count...

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 with more effective surveillance strategies to dete...

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 the opportunity for tailored screening and manageme...

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 burden in the United States. Neighborhood community-ba...

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 characteristics, and the lack of a vaccine or effec...

Yi Zeng: promoting good governance of artificial intelligence.

Artificial intelligence (AI) has developed quickly in recent years, with applications expanding from automatic driving and smart manufacturing to pers...

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 Syndrome Corona Virus 2) Coronavirus, unforeseen pand...

Using Machine Learning to Predict Suicide Attempts in Military Personnel.

Identifying predictors of suicide attempts is critical in intervention and prevention efforts, yet finding predictors has proven difficult due to the ...

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 all children worldwide experience violence every ...

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 informing clinical management. The coronavirus disease...

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 obtain. However, it requires further extraction and pr...